diff --git a/Numerics/Data/Interpolation/CubicSpline.cs b/Numerics/Data/Interpolation/CubicSpline.cs
index 804feb3a..6a32a3b1 100644
--- a/Numerics/Data/Interpolation/CubicSpline.cs
+++ b/Numerics/Data/Interpolation/CubicSpline.cs
@@ -28,9 +28,6 @@
 * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
 
-using System;
-using System.Collections.Generic;
-
 namespace Numerics.Data
 {
     /// <summary>
@@ -78,7 +75,7 @@ public CubicSpline(IList<double> xValues, IList<double> yValues, SortOrder sortO
         /// <summary>
         /// Stores the array of second derivatives.
         /// </summary>
-        private double[] y2;
+        private double[] y2 = Array.Empty<double>();
 
         /// <summary>
         /// Auxiliary routine to set the second derivatives. If you make changes to the x- or y-values, then you need to call this routine afterwards.
diff --git a/Numerics/Data/Paired Data/OrderedPairedData.cs b/Numerics/Data/Paired Data/OrderedPairedData.cs
index d269941f..8632cda5 100644
--- a/Numerics/Data/Paired Data/OrderedPairedData.cs	
+++ b/Numerics/Data/Paired Data/OrderedPairedData.cs	
@@ -35,6 +35,7 @@
 using System.Data;
 using System.Linq;
 using System.Xml.Linq;
+using System.Xml.Serialization;
 using Numerics.Distributions;
 
 namespace Numerics.Data
@@ -102,7 +103,7 @@ public class OrderedPairedData : IList<Ordinate>, INotifyCollectionChanged
         private bool _strictY;
         private SortOrder _orderX;
         private SortOrder _orderY;
-        private List<Ordinate> _ordinates;
+        private readonly List<Ordinate> _ordinates;
 
         /// <inheritdoc/>
         public event NotifyCollectionChangedEventHandler? CollectionChanged;
@@ -265,20 +266,24 @@ public OrderedPairedData(XElement el)
         {
             // Get Strictness
             bool strict = false;
-            if (el.Attribute(nameof(StrictX)) != null) { bool.TryParse(el.Attribute(nameof(StrictX)).Value, out strict); }
+            var strictXAttr = el.Attribute(nameof(StrictX));
+            if (strictXAttr != null) { bool.TryParse(strictXAttr.Value, out strict); }
             StrictX = strict;
 
             strict = false;
-            if (el.Attribute(nameof(StrictY)) != null) { bool.TryParse(el.Attribute(nameof(StrictY)).Value, out strict); }
+            var strictYAttr = el.Attribute(nameof(StrictY));
+            if (strictYAttr != null) { bool.TryParse(strictYAttr.Value, out strict); }
             StrictY = strict;
 
             // Get Order
             SortOrder order = SortOrder.None;
-            if (el.Attribute(nameof(OrderX)) != null) { Enum.TryParse(el.Attribute(nameof(OrderX)).Value, out order); }
+            var orderXAttr = el.Attribute(nameof(OrderX));
+            if (orderXAttr != null) { Enum.TryParse(orderXAttr.Value, out order); }
             OrderX = order;
 
             order = SortOrder.None;
-            if (el.Attribute(nameof(OrderY)) != null) { Enum.TryParse(el.Attribute(nameof(OrderY)).Value, out order); }
+            var orderYAttr = el.Attribute(nameof(OrderY));
+            if (orderYAttr != null) { Enum.TryParse(orderYAttr.Value, out order); }
             OrderY = order;
 
             // Ordinates
@@ -1432,15 +1437,14 @@ private double TriangleArea(Ordinate point1, Ordinate point2, Ordinate point3)
         /// and number of points in the search region.</returns>
         public OrderedPairedData LangSimplify(double tolerance, int lookAhead)
         {
-            if (_ordinates == null | lookAhead <= 1 | tolerance <= 0)
-                return this;
+            if (lookAhead <= 1 | tolerance <= 0) { return this; }
 
             List<Ordinate> ordinates = new List<Ordinate>();
 
             int count = _ordinates.Count;
             int offset;
-            if (lookAhead > count - 1)
-                lookAhead = count - 1;
+            if (lookAhead > count - 1) { lookAhead = count - 1; }
+
             ordinates.Add(_ordinates[0]);
 
             for (int i = 0; i < count; i++)
diff --git a/Numerics/Data/Paired Data/Ordinate.cs b/Numerics/Data/Paired Data/Ordinate.cs
index 8a7530cb..153787ae 100644
--- a/Numerics/Data/Paired Data/Ordinate.cs	
+++ b/Numerics/Data/Paired Data/Ordinate.cs	
@@ -79,9 +79,10 @@ public Ordinate(double xValue, double yValue)
         public Ordinate(XElement xElement)
         {
             double x = 0, y = 0;
-
-            if (xElement.Attribute(nameof(X)) != null) double.TryParse(xElement.Attribute(nameof(X))?.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out x);
-            if (xElement.Attribute(nameof(Y)) != null) double.TryParse(xElement.Attribute(nameof(Y))?.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out y);
+            var xAttribute = xElement.Attribute(nameof(X));
+            var yAttribute = xElement.Attribute(nameof(Y));
+            if (xAttribute != null) double.TryParse(xAttribute.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out x);
+            if (yAttribute != null) double.TryParse(yAttribute.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out y);
             X = x;
             Y = y;
             IsValid = true;
diff --git a/Numerics/Data/Paired Data/UncertainOrderedPairedData.cs b/Numerics/Data/Paired Data/UncertainOrderedPairedData.cs
index ed442c8d..4d9ab6a2 100644
--- a/Numerics/Data/Paired Data/UncertainOrderedPairedData.cs	
+++ b/Numerics/Data/Paired Data/UncertainOrderedPairedData.cs	
@@ -32,6 +32,7 @@
 using System.Collections;
 using System.Collections.Generic;
 using System.Collections.Specialized;
+using System.Data;
 using System.Globalization;
 using System.Linq;
 using System.Xml.Linq;
@@ -171,7 +172,7 @@ public SortOrder OrderY
         /// <summary>
         /// Handles the event of CollectionChanged
         /// </summary>
-        public event NotifyCollectionChangedEventHandler CollectionChanged;
+        public event NotifyCollectionChangedEventHandler? CollectionChanged;
 
         #endregion
 
@@ -243,7 +244,12 @@ public UncertainOrderedPairedData(IList<UncertainOrdinate> data, bool strictOnX,
             _orderY = yOrder;
             _uncertainOrdinates = new List<UncertainOrdinate>(data.Count);
             for (int i = 0; i < data.Count; i++)
-                _uncertainOrdinates.Add(new UncertainOrdinate(data[i].X, data[i].Y.Clone()));
+            {
+                var o = data[i];
+                UnivariateDistributionBase? yValue = o.Y?.Clone();
+                if (yValue is not null) { _uncertainOrdinates.Add(new UncertainOrdinate(o.X, yValue)); }
+            }
+    
             Validate();
         }
 
@@ -266,8 +272,12 @@ private UncertainOrderedPairedData(IList<UncertainOrdinate> data, bool strictOnX
             _orderY = yOrder;
             _uncertainOrdinates = new List<UncertainOrdinate>(data.Count);
             for (int i = 0; i < data.Count; i++)
-                _uncertainOrdinates.Add(new UncertainOrdinate(data[i].X, data[i].Y.Clone()));
-
+            {
+                var o = data[i];
+                UnivariateDistributionBase? yValue = o.Y?.Clone();
+                if (yValue is not null) { _uncertainOrdinates.Add(new UncertainOrdinate(o.X, yValue)); }
+            }
+                
             _isValid = dataValid;
         }
 
@@ -277,34 +287,44 @@ private UncertainOrderedPairedData(IList<UncertainOrdinate> data, bool strictOnX
         /// <param name="el">The XElement the UncertainOrderPairedData object is being created from.</param>
         public UncertainOrderedPairedData(XElement el)
         {
+            var strictX = el.Attribute("X_Strict");
             // Get Order
-            if (el.Attribute("X_Strict") != null)
-                bool.TryParse(el.Attribute("X_Strict").Value, out _strictX);
-            if (el.Attribute("Y_Strict") != null)
-                bool.TryParse(el.Attribute("Y_Strict").Value, out _strictY);
+            if (strictX != null) { bool.TryParse(strictX.Value, out _strictX); }
+
+            var strictY = el.Attribute("Y_Strict");
+            if (strictY != null) { bool.TryParse(strictY.Value, out _strictY); }
+
             // Get Strictness
-            if (el.Attribute("X_Order") != null)
-                Enum.TryParse(el.Attribute("X_Order").Value, out _orderX);
-            if (el.Attribute("Y_Order") != null)
-                Enum.TryParse(el.Attribute("Y_Order").Value, out _orderY);
+            var orderX = el.Attribute("X_Order");
+            if (orderX != null) { Enum.TryParse(orderX.Value, out _orderX); }
+
+            var orderY = el.Attribute("Y_Order");
+            if (orderY != null) { Enum.TryParse(orderY.Value, out _orderY); }
+
             // Distribution type
             Distribution = UnivariateDistributionType.Deterministic;
-            if (el.Attribute("Distribution") != null)
+            var distributionAttr = el.Attribute("Distribution");
+            if (distributionAttr != null)
             {
                 var argresult = Distribution;
-                Enum.TryParse(el.Attribute("Distribution").Value, out argresult);
+                Enum.TryParse(distributionAttr.Value, out argresult);
                 Distribution = argresult;
             }
             // new prop
-
-            if (el.Attribute(nameof(AllowDifferentDistributionTypes)) != null)
+            var allowDiffAtr = el.Attribute(nameof(AllowDifferentDistributionTypes));
+            if (allowDiffAtr != null)
             {
-                bool.TryParse(el.Attribute(nameof(AllowDifferentDistributionTypes)).Value, out _allowDifferentDistributionTypes);
+                bool.TryParse(allowDiffAtr.Value, out _allowDifferentDistributionTypes);
                 // Get Ordinates
                 var curveEl = el.Element("Ordinates");
                 _uncertainOrdinates = new List<UncertainOrdinate>();
-                foreach (XElement ord in curveEl.Elements(nameof(UncertainOrdinate)))
-                    _uncertainOrdinates.Add(new UncertainOrdinate(ord));
+
+                if (curveEl != null)
+                {
+                    foreach (XElement ord in curveEl.Elements(nameof(UncertainOrdinate)))
+                        _uncertainOrdinates.Add(new UncertainOrdinate(ord));
+                }
+               
             }
             else
             {
@@ -315,15 +335,19 @@ public UncertainOrderedPairedData(XElement el)
                 {
                     foreach (XElement o in curveEl.Elements("Ordinate"))
                     {
-                        double.TryParse(o.Attribute("X").Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var xout);
-                        xData.Add(xout);
+                        var xAttr = o.Attribute("X");
+                        if ( xAttr != null && double.TryParse(xAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var xout)) { xData.Add(xout); }
+                        else { xData.Add(0.0); }
+
                         var dist = UnivariateDistributionFactory.CreateDistribution(Distribution);
                         var props = dist.GetParameterPropertyNames;
                         var paramVals = new double[(props.Count())];
+
                         for (int i = 0; i < props.Count(); i++)
                         {
-                            double.TryParse(o.Attribute(props[i]).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var result);
-                            paramVals[i] = result;
+                            var pAttr = o.Attribute(props[i]);
+                            if ( pAttr != null && double.TryParse(pAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var result)) { paramVals[i] = result; }
+                            else { paramVals[i] = 0.0; }
                         }
 
                         dist.SetParameters(paramVals);
@@ -488,7 +512,14 @@ public List<string> GetErrors()
             {
                 if (left._uncertainOrdinates[i].X != right._uncertainOrdinates[i].X)
                     return false;
-                if (left._uncertainOrdinates[i].Y == right._uncertainOrdinates[i].Y == false)
+
+                var leftY = left._uncertainOrdinates[i].Y;
+                var rightY = right._uncertainOrdinates[i].Y;
+                if (leftY is null && rightY is null)
+                    continue;
+                if (leftY is null || rightY is null)
+                    return false;
+                if (!leftY.Equals(rightY))
                     return false;
             }
             return true;
@@ -510,7 +541,7 @@ public List<string> GetErrors()
         /// </summary>
         /// <param name="obj">The object to compare with the current object.</param>
         /// <returns>True if the specified object is equal to the current object; otherwise, False.</returns>
-        public override bool Equals(object obj)
+        public override bool Equals(object? obj)
         {
             if (obj is UncertainOrderedPairedData other)
             {
diff --git a/Numerics/Data/Paired Data/UncertainOrdinate.cs b/Numerics/Data/Paired Data/UncertainOrdinate.cs
index 1395f697..d3a81f7a 100644
--- a/Numerics/Data/Paired Data/UncertainOrdinate.cs	
+++ b/Numerics/Data/Paired Data/UncertainOrdinate.cs	
@@ -68,9 +68,7 @@ public UncertainOrdinate(double xValue, UnivariateDistributionBase yValue)
         {
             X = xValue;
             Y = yValue;
-            IsValid = true;
-            if (double.IsInfinity(X) || double.IsNaN(X) || Y == null || Y.ParametersValid == false)
-                IsValid = false;
+            IsValid = !(double.IsInfinity(X) || double.IsNaN(X) || Y is null || !Y.ParametersValid);
         }
 
         /// <summary>
@@ -79,16 +77,18 @@ public UncertainOrdinate(double xValue, UnivariateDistributionBase yValue)
         /// <param name="xElement">The XElement to deserialize.</param>
         public UncertainOrdinate(XElement xElement)
         {
+            var xAttr = xElement.Attribute(nameof(X));
             double x = 0;
-            UnivariateDistributionBase dist = null;
-            if (xElement.Attribute(nameof(X)) != null) double.TryParse(xElement.Attribute(nameof(X)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out x);
-            if (xElement.Element("Distribution") != null) { dist = UnivariateDistributionFactory.CreateDistribution(xElement.Element("Distribution")); }
+            UnivariateDistributionBase? dist = null;
+            if (xAttr != null) { double.TryParse(xAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out x); }
+
+            var distEl = xElement.Element("Distribution");
+            if (distEl != null) { dist = UnivariateDistributionFactory.CreateDistribution(distEl); }
             //
             X = x;
             Y = dist;
-            IsValid = true;
-            if (double.IsInfinity(X) || double.IsNaN(X) || Y == null || Y.ParametersValid == false)
-                IsValid = false;
+
+            IsValid = !(double.IsInfinity(X) || double.IsNaN(X) || Y is null || !Y.ParametersValid);
         }
 
         /// <summary>
@@ -99,7 +99,8 @@ public UncertainOrdinate(XElement xElement)
         public UncertainOrdinate(XElement xElement, UnivariateDistributionType distributionType)
         {
             double x = 0;
-            if (xElement.Attribute(nameof(X)) != null) double.TryParse(xElement.Attribute(nameof(X)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out x);
+            var xElAttr = xElement.Attribute(nameof(X));
+            if (xElAttr != null) double.TryParse(xElAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out x);
             // backwards compatibility
             var dist = UnivariateDistributionFactory.CreateDistribution(distributionType);
             var props = dist.GetParameterPropertyNames;
@@ -107,7 +108,8 @@ public UncertainOrdinate(XElement xElement, UnivariateDistributionType distribut
             for (int i = 0; i < props.Count(); i++)
             {
                 double p = 0;
-                if (xElement.Attribute(props[i]) != null) double.TryParse(xElement.Attribute(props[i]).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out p);
+                var xElPropsAttr = xElement.Attribute(props[i]);
+                if (xElPropsAttr != null) { double.TryParse(xElPropsAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out p); }
                 paramVals[i] = p;
             }
             dist.SetParameters(paramVals);
@@ -115,7 +117,7 @@ public UncertainOrdinate(XElement xElement, UnivariateDistributionType distribut
             X = x;
             Y = dist;
             IsValid = true;
-            if (double.IsInfinity(X) || double.IsNaN(X) || Y == null || Y.ParametersValid == false)
+            if (double.IsInfinity(X) || double.IsNaN(X) || Y is null || Y.ParametersValid == false)
                 IsValid = false;
         }
 
@@ -132,7 +134,7 @@ public UncertainOrdinate(XElement xElement, UnivariateDistributionType distribut
         /// <summary>
         /// Y distribution.
         /// </summary>
-        public UnivariateDistributionBase Y;
+        public UnivariateDistributionBase? Y;
 
         /// <summary>
         /// Boolean indicating if the ordinate has valid numeric values or not.
@@ -151,6 +153,10 @@ public UncertainOrdinate(XElement xElement, UnivariateDistributionType distribut
         /// <returns>A 'sampled' ordinate value.</returns>
         public Ordinate GetOrdinate(double probability)
         {
+            if (Y is null)
+            {
+                throw new InvalidOperationException("Y distribution is not defined.");
+            }
             return new Ordinate(X, Y.InverseCDF(probability));
         }
 
@@ -160,6 +166,10 @@ public Ordinate GetOrdinate(double probability)
         /// <returns>A mean ordinate value.</returns>
         public Ordinate GetOrdinate()
         {
+            if (Y is null)
+            {
+                throw new InvalidOperationException("Y distribution is not defined.");
+            }
             return new Ordinate(X, Y.Mean);
         }
 
@@ -182,9 +192,11 @@ public bool OrdinateValid(UncertainOrdinate ordinateToCompare, bool strictX, boo
             if (ordinateToCompare.IsValid == false)
                 return false;
             // Check for equivalent distribution types
-            if (allowDifferentTypes == false && ordinateToCompare.Y.Type != Y.Type)
+            if (allowDifferentTypes == false && (ordinateToCompare.Y is null || Y is null || ordinateToCompare.Y.Type != Y.Type))
                 return false;
 
+            if (Y is null || ordinateToCompare.Y is null)
+                return false;
             double minPercentile = Y.Type == UnivariateDistributionType.PertPercentile || Y.Type == UnivariateDistributionType.PertPercentileZ ? 0.05 : 1E-5;
 
             // Test reasonable lower bound
@@ -223,7 +235,7 @@ public List<string> OrdinateErrors(UncertainOrdinate ordinateToCompare, bool str
                     result.Add("Ordinate X value can not be infinity.");
                 if (double.IsNaN(ordinateToCompare.X))
                     result.Add("Ordinate X value must be a valid number.");
-                if (ordinateToCompare.Y == null)
+                if (ordinateToCompare.Y is null)
                 {
                     result.Add("Ordinate Y value must be defined.");
                 }
@@ -234,6 +246,12 @@ public List<string> OrdinateErrors(UncertainOrdinate ordinateToCompare, bool str
                 }
             }
             // Check for equivalent distribution types
+            if(ordinateToCompare.Y is null || Y is null)
+            {
+                result.Add("Ordinate Y value must be defined.");
+                return result;
+            }
+
             if (allowDifferentTypes == false && ordinateToCompare.Y.Type != Y.Type)
                 result.Add("Can't compare two ordinates with different distribution types."); // Return False
             // 
@@ -266,7 +284,7 @@ public List<string> OrdinateErrors()
                     result.Add("Ordinate X value can not be infinity.");
                 if (double.IsNaN(X))
                     result.Add("Ordinate X value must be a valid number.");
-                if (Y == null)
+                if (Y is null)
                     result.Add("Ordinate Y value must be defined.");
                 else if (Y.ParametersValid == false)
                 {
@@ -286,9 +304,13 @@ public List<string> OrdinateErrors()
         /// <returns>True if two objects are numerically equal; otherwise, False.</returns>
         public static bool operator ==(UncertainOrdinate left, UncertainOrdinate right)
         {
-            //if (left == null || right == null) return false;
+            
             if (left.X != right.X)
                 return false;
+            if (left.Y is null && right.Y is null)
+                return true;
+            if (left.Y is null || right.Y is null)
+                return false;
             if (left.Y != right.Y)
                 return false;
             return true;
@@ -310,7 +332,7 @@ public List<string> OrdinateErrors()
         /// </summary>
         /// <param name="obj">The object to compare with the current object.</param>
         /// <returns>True if the specified object is equal to the current object; otherwise, False.</returns>
-        public override bool Equals(object obj)
+        public override bool Equals(object? obj)
         {
             if (obj is UncertainOrdinate other)
             {
@@ -329,7 +351,7 @@ public override int GetHashCode()
             {
                 int hash = 17;
                 hash = hash * 23 + X.GetHashCode();
-                hash = hash * 23 + Y.GetHashCode();
+                hash = hash * 23 + (Y is not null ? Y.GetHashCode() : 0);
                 return hash;
             }
         }
@@ -341,7 +363,10 @@ public XElement ToXElement()
         {
             var result = new XElement(nameof(UncertainOrdinate));
             result.SetAttributeValue(nameof(X), X.ToString("G17", CultureInfo.InvariantCulture));
-            result.Add(Y.ToXElement());
+            if (Y is not null)
+            {
+                result.Add(Y.ToXElement());
+            }      
             return result;
         }
 
diff --git a/Numerics/Data/Regression/LinearRegression.cs b/Numerics/Data/Regression/LinearRegression.cs
index d33a0442..50ec19cd 100644
--- a/Numerics/Data/Regression/LinearRegression.cs
+++ b/Numerics/Data/Regression/LinearRegression.cs
@@ -117,32 +117,32 @@ public LinearRegression(Matrix x, Vector y, bool hasIntercept = true)
         /// <summary>
         /// The list of estimated parameter values.
         /// </summary>
-        public List<double> Parameters { get; private set; }
+        public List<double> Parameters { get; private set; } = Array.Empty<double>().ToList();
 
         /// <summary>
         /// The list of the estimated parameter names. 
         /// </summary>
-        public List<string> ParameterNames { get; private set; }
+        public List<string> ParameterNames { get; private set; } 
 
         /// <summary>
         /// The list of the estimated parameter standard errors. 
         /// </summary>
-        public List<double> ParameterStandardErrors { get; private set; }
+        public List<double> ParameterStandardErrors { get; private set; } = Array.Empty<double>().ToList();
 
         /// <summary>
         /// The list of the estimated parameter t-statistics.
         /// </summary>
-        public List<double> ParameterTStats { get; private set; }
+        public List<double> ParameterTStats { get; private set; } = Array.Empty<double>().ToList();
 
         /// <summary>
         /// The estimate parameter covariance matrix. 
         /// </summary>
-        public Matrix Covariance { get; private set; }
+        public Matrix Covariance { get; private set; } = new Matrix(0, 0);
 
         /// <summary>
         /// The residuals of the fitted linear model. 
         /// </summary>
-        public double[] Residuals { get; private set; }
+        public double[] Residuals { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// The model standard error.
diff --git a/Numerics/Data/Statistics/Autocorrelation.cs b/Numerics/Data/Statistics/Autocorrelation.cs
index c2f08cd2..c08daec1 100644
--- a/Numerics/Data/Statistics/Autocorrelation.cs
+++ b/Numerics/Data/Statistics/Autocorrelation.cs
@@ -94,7 +94,7 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the function evaluated at the given values.
         /// </returns>
-        public static double[,] Function(IList<double> data, int lagMax = -1, Type type = Type.Correlation)
+        public static double[,]? Function(IList<double> data, int lagMax = -1, Type type = Type.Correlation)
         {
             if (type == Type.Correlation)
             {
@@ -123,7 +123,7 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the function evaluated at the given values.
         /// </returns>
-        public static double[,] Function(TimeSeries timeSeries, int lagMax = -1, Type type = Type.Correlation)
+        public static double[,]? Function(TimeSeries timeSeries, int lagMax = -1, Type type = Type.Correlation)
         {
             if (type == Type.Correlation)
             {
@@ -150,7 +150,7 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the covariance of the given values.
         /// </returns>
-        private static double[,] Covariance(IList<double> data, int lagMax = -1)
+        private static double[,]? Covariance(IList<double> data, int lagMax = -1)
         {
             int n = data.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
@@ -177,7 +177,7 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the covariance of the given values.
         /// </returns>
-        private static double[,] Covariance(TimeSeries timeSeries, int lagMax = -1)
+        private static double[,]? Covariance(TimeSeries timeSeries, int lagMax = -1)
         {
             int n = timeSeries.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
@@ -205,12 +205,15 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the autocorrelation of the given values.
         /// </returns>
-        private static double[,] Correlation(IList<double> data, int lagMax = -1)
+        private static double[,]? Correlation(IList<double> data, int lagMax = -1)
         {
             int n = data.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
             if (lagMax < 1 || n < 2) return null;
             var acf = Covariance(data, lagMax);
+
+            if (acf == null) return null;
+
             double den = acf[0, 1];
             for (int i = 0; i < acf.GetLength(0); i++)
                 acf[i, 1] /= den;
@@ -226,12 +229,15 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the autocorrelation of the given values.
         /// </returns>
-        private static double[,] Correlation(TimeSeries timeSeries, int lagMax = -1)
+        private static double[,]? Correlation(TimeSeries timeSeries, int lagMax = -1)
         {
             int n = timeSeries.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
             if (lagMax < 1 || n < 2) return null;
+
             var acf = Covariance(timeSeries, lagMax);
+            if (acf == null) return null;
+
             double den = acf[0, 1];
             for (int i = 0; i < acf.GetLength(0); i++)
                 acf[i, 1] /= den;
@@ -247,13 +253,14 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the partial autocorrelation of the given values.
         /// </returns>
-        private static double[,] Partial(IList<double> data, int lagMax = -1)
+        private static double[,]? Partial(IList<double> data, int lagMax = -1)
         {
             int n = data.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
             if (lagMax < 1 || n < 2) return null;
             // First compute the ACVF
             var acvf = Covariance(data, lagMax);
+            if (acvf == null) return null;
             // Then compute PACF using the Durbin-Levinson algorithm
             int i, j;
             var phis = new double[lagMax + 1];
@@ -293,13 +300,14 @@ public enum Type
         /// A n x 2 matrix, with being the number of given input data points. The first column contains the lag and the 
         /// second column contains the partial autocorrelation of the given values.
         /// </returns>
-        private static double[,] Partial(TimeSeries timeSeries, int lagMax = -1)
+        private static double[,]? Partial(TimeSeries timeSeries, int lagMax = -1)
         {
             int n = timeSeries.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
             if (lagMax < 1 || n < 2) return null;
             // First compute the ACVF
             var acvf = Covariance(timeSeries, lagMax);
+            if (acvf == null) return null;
             // Then compute PACF using the Durbin-Levinson algorithm
             int i, j;
             var phis = new double[lagMax + 1];
diff --git a/Numerics/Data/Statistics/Histogram.cs b/Numerics/Data/Statistics/Histogram.cs
index 1ff14e83..201ff926 100644
--- a/Numerics/Data/Statistics/Histogram.cs
+++ b/Numerics/Data/Statistics/Histogram.cs
@@ -119,8 +119,10 @@ public double Midpoint
             /// +1 if this bin is lower than the compared bin.
             /// -1 otherwise.
             /// </returns>
-            public int CompareTo(Bin other)
+            public int CompareTo(Bin? other)
             {
+                if (other is null) { return 1; }
+
                 if (UpperBound > other.LowerBound && LowerBound < other.LowerBound)
                 {
                     throw new ArgumentException(nameof(other), "The bins cannot be overlapping.");
@@ -149,7 +151,7 @@ public object Clone()
             /// Checks whether two histogram bins are equal.
             /// </summary>
             /// <returns>True if the bins are equal and false otherwise.</returns>
-            public override bool Equals(object obj)
+            public override bool Equals(object? obj)
             {
                 if (!(obj is Bin))
                 {
diff --git a/Numerics/Data/Statistics/HypothesisTests.cs b/Numerics/Data/Statistics/HypothesisTests.cs
index 97d00084..6c50254b 100644
--- a/Numerics/Data/Statistics/HypothesisTests.cs
+++ b/Numerics/Data/Statistics/HypothesisTests.cs
@@ -245,6 +245,7 @@ public static double LjungBoxTest(IList<double> sample, int lagMax = -1)
             int n = sample.Count;
             if (lagMax < 0) lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
             var acf = Autocorrelation.Function(sample, lagMax, Autocorrelation.Type.Correlation);
+            if (acf == null) throw new Exception("Autocorrelation function could not be calculated.");
             double Q = 0;
             for (int k = 1; k <= lagMax; k++)
                 Q += Tools.Sqr(acf[k, 1]) / (n - k);
diff --git a/Numerics/Data/Statistics/PlottingPositions.cs b/Numerics/Data/Statistics/PlottingPositions.cs
index fac4557c..aa74f137 100644
--- a/Numerics/Data/Statistics/PlottingPositions.cs
+++ b/Numerics/Data/Statistics/PlottingPositions.cs
@@ -82,7 +82,7 @@ public static double[] Function(int N, double alpha)
         /// <param name="N">The sample size.</param>
         /// <param name="plottingPostionType">The plotting position formula type.</param>
         /// <returns>An array of plotting positions of size N.</returns>
-        public static double[] Function(int N, PlottingPositions.PlottingPostionType plottingPostionType)
+        public static double[]? Function(int N, PlottingPositions.PlottingPostionType plottingPostionType)
         {
             if (plottingPostionType == PlottingPostionType.Blom)
             {
diff --git a/Numerics/Data/Statistics/Probability.cs b/Numerics/Data/Statistics/Probability.cs
index 9f33dbb3..77e9c4c6 100644
--- a/Numerics/Data/Statistics/Probability.cs
+++ b/Numerics/Data/Statistics/Probability.cs
@@ -184,7 +184,7 @@ public static double JointProbability(IList<double> probabilities, DependencyTyp
         /// <param name="correlationMatrix">The correlation matrix defining the dependency. Default = null.</param>
         /// <param name="dependency">The dependency type. Default = Correlation matrix.</param>
         /// <returns>The joint probability.</returns>
-        public static double JointProbability(IList<double> probabilities, int[] indicators, double[,] correlationMatrix = null, DependencyType dependency = DependencyType.CorrelationMatrix)
+        public static double JointProbability(IList<double> probabilities, int[] indicators, double[,]? correlationMatrix = null, DependencyType dependency = DependencyType.CorrelationMatrix)
         {
             if (dependency == DependencyType.CorrelationMatrix && correlationMatrix != null)
             {
@@ -312,7 +312,7 @@ public static double NegativeJointProbability(IList<double> probabilities, int[]
         /// <remarks>
         /// This method utilizes a modified version of Pandey's PCM method.
         /// </remarks>
-        public static double JointProbabilityHPCM(IList<double> probabilities, int[] indicators, double[,] correlationMatrix, double[] conditionalProbabilities = null)
+        public static double JointProbabilityHPCM(IList<double> probabilities, int[] indicators, double[,] correlationMatrix, double[]? conditionalProbabilities = null)
         {
             // Validation Checks
             if (probabilities == null || probabilities.Count == 0)
@@ -431,7 +431,7 @@ public static double JointProbabilityHPCM(IList<double> probabilities, int[] ind
         /// <returns>
         /// The joint probability of the events, adjusted for dependencies as defined by the correlation matrix. The return value is between 0 and 1.
         /// </returns>
-        public static double JointProbabilityPCM(IList<double> probabilities, int[] indicators, double[,] correlationMatrix, double[] conditionalProbabilities = null)
+        public static double JointProbabilityPCM(IList<double> probabilities, int[] indicators, double[,] correlationMatrix, double[]? conditionalProbabilities = null)
         {
             // Validation Checks
             if (probabilities == null || probabilities.Count == 0)
@@ -1899,7 +1899,7 @@ public static double CommonCauseAdjustment(IList<double> probabilities)
         /// <param name="correlationMatrix">The correlation matrix defining the dependency.</param>
         /// <param name="dependency">The dependency type. Default = Correlation matrix.</param>
         /// <returns>The common cause adjustment factor.</returns>
-        public static double CommonCauseAdjustment(IList<double> probabilities, double[,] correlationMatrix = null, DependencyType dependency = DependencyType.CorrelationMatrix)
+        public static double CommonCauseAdjustment(IList<double> probabilities, double[,]? correlationMatrix = null, DependencyType dependency = DependencyType.CorrelationMatrix)
         {
             // Validation Checks
             if (probabilities == null || probabilities.Count == 0)
diff --git a/Numerics/Data/Statistics/Statistics.cs b/Numerics/Data/Statistics/Statistics.cs
index a45dc06d..1ab3e9af 100644
--- a/Numerics/Data/Statistics/Statistics.cs
+++ b/Numerics/Data/Statistics/Statistics.cs
@@ -368,7 +368,7 @@ public static double JackKnifeStandardError(IList<double> data, Func<IList<doubl
         /// </summary>
         /// <param name="data">Sample of data, no sorting is assumed.</param>
         /// <param name="statistic">The statistic for estimating a sample.</param>
-        public static double[] JackKnifeSample(IList<double> data, Func<IList<double>, double> statistic)
+        public static double[]? JackKnifeSample(IList<double> data, Func<IList<double>, double> statistic)
         {
             if (data == null) throw new ArgumentNullException(nameof(data));
             if (data.Count == 0) return null;
diff --git a/Numerics/Data/Time Series/Support/Series.cs b/Numerics/Data/Time Series/Support/Series.cs
index 05685a06..a78cd46f 100644
--- a/Numerics/Data/Time Series/Support/Series.cs	
+++ b/Numerics/Data/Time Series/Support/Series.cs	
@@ -56,7 +56,7 @@ public abstract class Series<TIndex, TValue> : IList<SeriesOrdinate<TIndex, TVal
         protected List<SeriesOrdinate<TIndex, TValue>> _seriesOrdinates = new List<SeriesOrdinate<TIndex, TValue>>();
 
         /// <inheritdoc/>
-        public event NotifyCollectionChangedEventHandler CollectionChanged;
+        public event NotifyCollectionChangedEventHandler? CollectionChanged;
 
         /// <inheritdoc/>
         public SeriesOrdinate<TIndex, TValue> this[int index]
@@ -75,11 +75,13 @@ public SeriesOrdinate<TIndex, TValue> this[int index]
         }
 
         /// <inheritdoc/>
-        object IList.this[int index]
+        object? IList.this[int index]
         {
             get { return _seriesOrdinates[index]; }
             set
             {
+                if (value is null) { throw new ArgumentNullException(nameof(value)); }
+
                 if (value.GetType() != typeof(SeriesOrdinate<TIndex, TValue>))
                 {
                     if (_seriesOrdinates[index] != (SeriesOrdinate<TIndex, TValue>)value)
@@ -109,7 +111,7 @@ object IList.this[int index]
         public bool IsFixedSize => false;
 
         /// <inheritdoc/>
-        public object SyncRoot => _seriesOrdinates.Count > 0 ? _seriesOrdinates[0] : null;
+        public object SyncRoot => _seriesOrdinates.Count > 0 ? _seriesOrdinates[0]! : new object();
 
         /// <inheritdoc/>
         public bool IsSynchronized => false;
@@ -117,14 +119,15 @@ object IList.this[int index]
         /// <inheritdoc/>
         public virtual void Add(SeriesOrdinate<TIndex, TValue> item)
         {
-            if (item == null) throw new ArgumentNullException(nameof(item));
+            if (item is null) throw new ArgumentNullException(nameof(item));
             _seriesOrdinates.Add(item);         
             RaiseCollectionChanged(new NotifyCollectionChangedEventArgs(NotifyCollectionChangedAction.Add, item, _seriesOrdinates.Count - 1)); 
         }
 
         /// <inheritdoc/>
-        public int Add(object item)
+        public int Add(object? item)
         {
+            if (item is null) throw new ArgumentNullException(nameof(item));
             if (item.GetType() != typeof(SeriesOrdinate<TIndex, TValue>)) { return -1; }
             Add((SeriesOrdinate<TIndex, TValue>)item);
             return _seriesOrdinates.Count - 1;
@@ -133,14 +136,15 @@ public int Add(object item)
         /// <inheritdoc/>
         public virtual void Insert(int index, SeriesOrdinate<TIndex, TValue> item)
         {
-            if (item == null) throw new ArgumentNullException(nameof(item));
+            if (item is null) throw new ArgumentNullException(nameof(item));
             _seriesOrdinates.Insert(index, item);
             RaiseCollectionChanged(new NotifyCollectionChangedEventArgs(NotifyCollectionChangedAction.Add, item, index));
         }
 
         /// <inheritdoc/>
-        public void Insert(int index, object item)
+        public void Insert(int index, object? item)
         {
+            if (item is null) throw new ArgumentNullException(nameof(item));
             if (item.GetType() == typeof(SeriesOrdinate<TIndex, TValue>)) 
             { 
                 Insert(index, (SeriesOrdinate<TIndex, TValue>)item); 
@@ -160,8 +164,9 @@ public virtual bool Remove(SeriesOrdinate<TIndex, TValue> item)
         }
 
         /// <inheritdoc/>
-        public void Remove(object item)
+        public void Remove(object? item)
         {
+            if (item is null) throw new ArgumentNullException(nameof(item));
             if (item.GetType() == typeof(SeriesOrdinate<TIndex, TValue>))
             {
                 Remove((SeriesOrdinate<TIndex, TValue>)item);
@@ -195,8 +200,9 @@ public bool Contains(SeriesOrdinate<TIndex, TValue> item)
         }
 
         /// <inheritdoc/>
-        public bool Contains(object item)
+        public bool Contains(object? item)
         {
+            if (item is null) throw new ArgumentNullException(nameof(item));
             if (item.GetType() == typeof(SeriesOrdinate<TIndex, TValue>))
             {
                 return Contains((SeriesOrdinate<TIndex, TValue>)item);
@@ -226,8 +232,9 @@ public int IndexOf(SeriesOrdinate<TIndex, TValue> item)
         }
 
         /// <inheritdoc/>
-        public int IndexOf(object item)
+        public int IndexOf(object? item)
         {
+            if (item is null) throw new ArgumentNullException(nameof(item));
             if (item.GetType() == typeof(SeriesOrdinate<TIndex, TValue>))
             {
                 return _seriesOrdinates.IndexOf((SeriesOrdinate<TIndex, TValue>)item);
diff --git a/Numerics/Data/Time Series/Support/SeriesOrdinate.cs b/Numerics/Data/Time Series/Support/SeriesOrdinate.cs
index 9dc8c2d8..8cbde3f1 100644
--- a/Numerics/Data/Time Series/Support/SeriesOrdinate.cs	
+++ b/Numerics/Data/Time Series/Support/SeriesOrdinate.cs	
@@ -50,7 +50,11 @@ public class SeriesOrdinate<TIndex, TValue> : INotifyPropertyChanged, IEquatable
         /// <summary>
         /// Constructs a new series ordinate.
         /// </summary>
-        public SeriesOrdinate() { }
+        public SeriesOrdinate() 
+        {
+            _index = default!;
+            _value = default!;
+        }
 
         /// <summary>
         /// Constructs a new series ordinate. 
@@ -74,7 +78,7 @@ public SeriesOrdinate(TIndex index, TValue value)
         protected TValue _value;
 
         /// <inheritdoc/>
-        public event PropertyChangedEventHandler PropertyChanged;
+        public event PropertyChangedEventHandler? PropertyChanged;
 
         /// <summary>
         /// The index of the series ordinate.
@@ -109,7 +113,7 @@ public virtual TValue Value
         }
 
         /// <inheritdoc/>
-        public bool Equals(SeriesOrdinate<TIndex, TValue> other)
+        public bool Equals(SeriesOrdinate<TIndex, TValue>? other)
         {
             if (ReferenceEquals(other, null)) return false;
             if (ReferenceEquals(this, other)) return true;
@@ -118,7 +122,7 @@ public bool Equals(SeriesOrdinate<TIndex, TValue> other)
         }
 
         /// <inheritdoc/>
-        public override bool Equals(object obj) => Equals(obj as SeriesOrdinate<TIndex, TValue>);
+        public override bool Equals(object? obj) => Equals((SeriesOrdinate<TIndex, TValue>?)obj);
 
         /// <summary>
         /// Equality operator overload. 
@@ -143,8 +147,8 @@ public override int GetHashCode()
             unchecked
             {
                 int hash = 17;
-                hash = hash * 23 + EqualityComparer<TIndex>.Default.GetHashCode(_index);
-                hash = hash * 23 + EqualityComparer<TValue>.Default.GetHashCode(_value);
+                hash = hash * 23 + (_index is null ? 0 : EqualityComparer<TIndex>.Default.GetHashCode(_index));
+                hash = hash * 23 + (_value is null ? 0 : EqualityComparer<TValue>.Default.GetHashCode(_value));
                 return hash;
             }
         }
diff --git a/Numerics/Data/Time Series/Support/TimeSeriesDownload.cs b/Numerics/Data/Time Series/Support/TimeSeriesDownload.cs
index 41f3ea1b..dff08c99 100644
--- a/Numerics/Data/Time Series/Support/TimeSeriesDownload.cs	
+++ b/Numerics/Data/Time Series/Support/TimeSeriesDownload.cs	
@@ -173,11 +173,7 @@ public enum HeightUnit
         /// <returns>A downloaded time series.</returns>
         public static async Task<TimeSeries> FromGHCN(string siteNumber, TimeSeriesType timeSeriesType = TimeSeriesType.DailyPrecipitation, DepthUnit unit = DepthUnit.Inches)
         {
-            // Check internet connection
-            if (!await IsConnectedToInternet())
-            {
-                throw new InvalidOperationException("No internet connection.");
-            }
+            
 
             // Check site number
             if (siteNumber.Length != 11)
@@ -195,6 +191,12 @@ public static async Task<TimeSeries> FromGHCN(string siteNumber, TimeSeriesType
             DateTime? previousDate = null;
             string tempFilePath = Path.Combine(Path.GetTempPath(), $"{siteNumber}.dly");
 
+            // Check internet connection
+            if (!await IsConnectedToInternet())
+            {
+                throw new InvalidOperationException("No internet connection.");
+            }
+
             try
             {
  
@@ -353,11 +355,7 @@ private static string CreateURLForUSGSDownload(string siteNumber, TimeSeriesType
         /// <param name="timeSeriesType">The time series type.</param>
         public static async Task<(TimeSeries TimeSeries, string RawText)> FromUSGS(string siteNumber, TimeSeriesType timeSeriesType = TimeSeriesType.DailyDischarge)
         {
-            // Check internet connection
-            if (!await IsConnectedToInternet())
-            {
-                throw new InvalidOperationException("No internet connection.");
-            }
+            
 
             // Check site number
             if (siteNumber.Length != 8)
@@ -371,6 +369,12 @@ private static string CreateURLForUSGSDownload(string siteNumber, TimeSeriesType
                 throw new ArgumentException("The time series type cannot be daily precipitation or daily snow.", nameof(timeSeriesType));
             }
 
+            // Check internet connection
+            if (!await IsConnectedToInternet())
+            {
+                throw new InvalidOperationException("No internet connection.");
+            }
+
             var timeSeries = new TimeSeries();
             string textDownload = "";
 
@@ -403,7 +407,7 @@ private static string CreateURLForUSGSDownload(string siteNumber, TimeSeriesType
                         using (GZipStream decompressionStream = new GZipStream(compressedStream, CompressionMode.Decompress))
                         using (StreamReader reader = new StreamReader(decompressionStream))
                         {
-                            string line;
+                            string? line;
                             bool isHeader = true;
 
                             while ((line = await reader.ReadLineAsync()) != null)
@@ -742,9 +746,7 @@ public static async Task<TimeSeries> FromABOM(
             DateTime? startDate = null,
             DateTime? endDate = null)
         {
-            // Check connectivity
-            if (!await IsConnectedToInternet())
-                throw new InvalidOperationException("No internet connection.");
+            
 
             // Validate station number (BOM station numbers are typically 6 digits)
             if (string.IsNullOrWhiteSpace(stationNumber) || stationNumber.Length < 6)
@@ -754,6 +756,10 @@ public static async Task<TimeSeries> FromABOM(
             if (timeSeriesType != TimeSeriesType.DailyDischarge && timeSeriesType != TimeSeriesType.DailyStage)
                 throw new ArgumentException("BOM API supports DailyDischarge or DailyStage only.", nameof(timeSeriesType));
 
+            // Check connectivity
+            if (!await IsConnectedToInternet())
+                throw new InvalidOperationException("No internet connection.");
+
             // Set default dates
             DateTime sd = startDate ?? new DateTime(1800, 1, 1);
             DateTime ed = endDate ?? DateTime.Today;
@@ -770,7 +776,7 @@ public static async Task<TimeSeries> FromABOM(
                 $"&station_no={Uri.EscapeDataString(stationNumber)}" +
                 $"&parametertype_name={Uri.EscapeDataString(parameterType)}";
 
-            string tsId = null;
+            string? tsId = null;
 
             // Create HttpClientHandler with automatic decompression
             var handler = new HttpClientHandler
@@ -840,7 +846,7 @@ public static async Task<TimeSeries> FromABOM(
 
                 for (int i = 0; i < headers.GetArrayLength(); i++)
                 {
-                    string header = headers[i].GetString();
+                    string? header = headers[i].GetString();
                     if (header == "ts_id") tsIdIndex = i;
                     if (header == "ts_name") tsNameIndex = i;
                 }
@@ -852,8 +858,9 @@ public static async Task<TimeSeries> FromABOM(
                 for (int i = 1; i < root.GetArrayLength(); i++)
                 {
                     var row = root[i];
-                    string tsName = tsNameIndex >= 0 ? row[tsNameIndex].GetString() : "";
+                    string? tsName = tsNameIndex >= 0 ? row[tsNameIndex].GetString() : "";
 
+                    if (tsName == null) continue;
                     // Prioritize: DMQaQc.Merged.DailyMean.24HR or similar daily mean series
                     if (tsName.Contains("DailyMean") || tsName.Contains("Daily Mean"))
                     {
@@ -940,7 +947,7 @@ public static async Task<TimeSeries> FromABOM(
                     if (point.GetArrayLength() < 2) continue;
 
                     // Parse timestamp
-                    string timestampStr = point[0].GetString();
+                    string? timestampStr = point[0].GetString();
                     if (!DateTime.TryParse(timestampStr, out DateTime date))
                         continue;
 
diff --git a/Numerics/Data/Time Series/TimeSeries.cs b/Numerics/Data/Time Series/TimeSeries.cs
index ec18039a..53f1bb4d 100644
--- a/Numerics/Data/Time Series/TimeSeries.cs	
+++ b/Numerics/Data/Time Series/TimeSeries.cs	
@@ -141,20 +141,32 @@ public TimeSeries(TimeInterval timeInterval, DateTime startDate, IList<double> d
         public TimeSeries(XElement xElement)
         {
             // Get time interval
-            if (xElement.Attribute(nameof(TimeInterval)) != null)
-                Enum.TryParse(xElement.Attribute(nameof(TimeInterval)).Value, out _timeInterval);
+            var timeIntervalAttr = xElement.Attribute(nameof(TimeInterval));
+            if (timeIntervalAttr != null)
+                Enum.TryParse(timeIntervalAttr.Value, out _timeInterval);
 
             // Get Ordinates
             foreach (XElement ordinate in xElement.Elements("SeriesOrdinate"))
             {
                 // Try to parse the invariant date string using TryParseExact
                 // If it fails, do a regular try parse.
-                DateTime index;
-                if (!DateTime.TryParseExact(ordinate.Attribute("Index").Value, "o", CultureInfo.InvariantCulture, DateTimeStyles.RoundtripKind, out index))
+                DateTime index = default;
+                var ordAttr = ordinate.Attribute("Index");
+                if (ordAttr != null)
                 {
-                    DateTime.TryParse(ordinate.Attribute("Index").Value, out index);
+                    if (!DateTime.TryParseExact(ordAttr.Value, "o", CultureInfo.InvariantCulture, DateTimeStyles.RoundtripKind, out index))
+                    {
+                        DateTime.TryParse(ordAttr.Value, out index);
+                    }
+                }
+
+                double value = 0.0;
+                var ordVal = ordinate.Attribute("Value");
+                if (ordVal != null)
+                {
+                    double.TryParse(ordVal.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out value);
                 }
-                double.TryParse(ordinate.Attribute("Value").Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var value);
+                
                 Add(new SeriesOrdinate<DateTime, double>(index, value));
             }
         }
@@ -1115,7 +1127,7 @@ public TimeSeries ClipTimeSeries(DateTime startDate, DateTime endDate)
         /// <param name="timeInterval">The new time interval.</param>
         /// <param name="average">Optional. Determines if values should be averaged (true) or cumulated (false) for larger time steps. Default = true.</param>
         /// <returns>A new TimeSeries object with the new interval.</returns>
-        public TimeSeries ConvertTimeInterval(TimeInterval timeInterval, bool average = true)
+        public TimeSeries? ConvertTimeInterval(TimeInterval timeInterval, bool average = true)
         {
             var TS = TimeSeries.TimeIntervalInHours(TimeInterval); // The time step in hours
             var newTS = TimeSeries.TimeIntervalInHours(timeInterval); // The new time step in hours
@@ -1467,7 +1479,7 @@ public TimeSeries CalendarYearSeries(BlockFunctionType blockFunction = BlockFunc
             var result = new TimeSeries(TimeInterval.Irregular);
 
             // First, perform smoothing function
-            TimeSeries smoothedSeries = null;
+            TimeSeries? smoothedSeries = null;
             if (smoothingFunction == SmoothingFunctionType.None)
             {
                 smoothedSeries = Clone();
@@ -1486,6 +1498,7 @@ public TimeSeries CalendarYearSeries(BlockFunctionType blockFunction = BlockFunc
             }
 
             // Then, perform block function
+            if (smoothedSeries == null) return result;
             for (int i = smoothedSeries.StartDate.Year; i <= smoothedSeries.EndDate.Year; i++)
             {
                 var blockData = smoothedSeries.Where(x => x.Index.Year == i).ToList();
@@ -1561,7 +1574,7 @@ public TimeSeries CustomYearSeries(int startMonth = 10, BlockFunctionType blockF
             var result = new TimeSeries(TimeInterval.Irregular);
 
             // First, perform smoothing function
-            TimeSeries smoothedSeries = null;
+            TimeSeries? smoothedSeries = null;
             if (smoothingFunction == SmoothingFunctionType.None)
             {
                 smoothedSeries = Clone();
@@ -1580,6 +1593,7 @@ public TimeSeries CustomYearSeries(int startMonth = 10, BlockFunctionType blockF
             }
 
             // Then, shift the dates
+            if( smoothedSeries == null) return result;
             int shift = startMonth != 1 ? 12 - startMonth + 1 : 0;
             smoothedSeries = startMonth != 1 ? smoothedSeries.ShiftDatesByMonth(shift) : smoothedSeries;
 
@@ -1667,7 +1681,7 @@ public TimeSeries CustomYearSeries(int startMonth, int endMonth, BlockFunctionTy
             var result = new TimeSeries(TimeInterval.Irregular);
 
             // First, perform smoothing function
-            TimeSeries smoothedSeries = null;
+            TimeSeries? smoothedSeries = null;
             if (smoothingFunction == SmoothingFunctionType.None)
             {
                 smoothedSeries = Clone();
@@ -1686,6 +1700,7 @@ public TimeSeries CustomYearSeries(int startMonth, int endMonth, BlockFunctionTy
             }
 
             // Then, perform block function
+            if(smoothedSeries == null) return result;
             for (int i = smoothedSeries.StartDate.Year; i <= smoothedSeries.EndDate.Year; i++)
             {
 
@@ -1779,7 +1794,7 @@ public TimeSeries MonthlySeries(BlockFunctionType blockFunction = BlockFunctionT
             var result = new TimeSeries(TimeInterval.Irregular);
 
             // Create smoothed series
-            TimeSeries smoothedSeries = null;
+            TimeSeries? smoothedSeries = null;
             if (smoothingFunction == SmoothingFunctionType.None)
             {
                 smoothedSeries = Clone();
@@ -1797,6 +1812,7 @@ public TimeSeries MonthlySeries(BlockFunctionType blockFunction = BlockFunctionT
                 smoothedSeries = Difference(period);
             }
 
+            if(smoothedSeries == null) return result;
             for (int i = smoothedSeries.StartDate.Year; i <= smoothedSeries.EndDate.Year; i++)
             {
 
@@ -1875,7 +1891,7 @@ public TimeSeries QuarterlySeries(BlockFunctionType blockFunction = BlockFunctio
             var result = new TimeSeries(TimeInterval.Irregular);
 
             // Create smoothed series
-            TimeSeries smoothedSeries = null;
+            TimeSeries? smoothedSeries = null;
             if (smoothingFunction == SmoothingFunctionType.None)
             {
                 smoothedSeries = Clone();
@@ -1893,6 +1909,7 @@ public TimeSeries QuarterlySeries(BlockFunctionType blockFunction = BlockFunctio
                 smoothedSeries = Difference(period);
             }
 
+            if (smoothedSeries == null) return result;
             for (int i = smoothedSeries.StartDate.Year; i <= smoothedSeries.EndDate.Year; i++)
             {
 
@@ -1987,7 +2004,7 @@ public TimeSeries QuarterlySeries(BlockFunctionType blockFunction = BlockFunctio
         public TimeSeries PeaksOverThresholdSeries(double threshold, int minStepsBetweenEvents = 1, SmoothingFunctionType smoothingFunction = SmoothingFunctionType.None, int period = 1)
         {
             // Create smoothed time series
-            TimeSeries smoothedSeries = null;
+            TimeSeries? smoothedSeries = null;
             if (smoothingFunction == SmoothingFunctionType.None)
             {
                 smoothedSeries = Clone();
@@ -2009,6 +2026,7 @@ public TimeSeries PeaksOverThresholdSeries(double threshold, int minStepsBetween
             int i = 0, idx, idxMax;
             var clusters = new List<int[]>();
 
+            if(smoothedSeries == null) return new TimeSeries(TimeInterval.Irregular);
             while (i < smoothedSeries.Count)
             {
                 if (!double.IsNaN(smoothedSeries[i].Value) && smoothedSeries[i].Value > threshold)
diff --git a/Numerics/Distributions/Bivariate Copulas/AMHCopula.cs b/Numerics/Distributions/Bivariate Copulas/AMHCopula.cs
index e1239d57..3b6ab3da 100644
--- a/Numerics/Distributions/Bivariate Copulas/AMHCopula.cs	
+++ b/Numerics/Distributions/Bivariate Copulas/AMHCopula.cs	
@@ -122,7 +122,7 @@ public override ArgumentOutOfRangeException ValidateParameter(double parameter,
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(Theta), "The dependency parameter θ (theta) must be less than or equal to " + ThetaMaximum.ToString() + ".");
                 return new ArgumentOutOfRangeException(nameof(Theta), "The dependency parameter θ (theta) must be less than or equal to " + ThetaMaximum.ToString() + ".");
             }
-            return null;
+            return new ArgumentOutOfRangeException(nameof(Theta), "Parameter is valid.");
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Bivariate Copulas/Base/ArchimedeanCopula.cs b/Numerics/Distributions/Bivariate Copulas/Base/ArchimedeanCopula.cs
index 6bd55b8e..5b23539a 100644
--- a/Numerics/Distributions/Bivariate Copulas/Base/ArchimedeanCopula.cs	
+++ b/Numerics/Distributions/Bivariate Copulas/Base/ArchimedeanCopula.cs	
@@ -78,7 +78,7 @@ public override ArgumentOutOfRangeException ValidateParameter(double parameter,
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(Theta), "The dependency parameter θ (theta) must be less than or equal to " + ThetaMaximum.ToString() + ".");
                 return new ArgumentOutOfRangeException(nameof(Theta), "The dependency parameter θ (theta) must be less than or equal to " + ThetaMaximum.ToString() + ".");
             }
-            return null;
+            return new ArgumentOutOfRangeException(nameof(Theta),"Parameter is valid");
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Bivariate Copulas/Base/BivariateCopula.cs b/Numerics/Distributions/Bivariate Copulas/Base/BivariateCopula.cs
index 4015be24..60944869 100644
--- a/Numerics/Distributions/Bivariate Copulas/Base/BivariateCopula.cs	
+++ b/Numerics/Distributions/Bivariate Copulas/Base/BivariateCopula.cs	
@@ -108,10 +108,10 @@ public bool ParametersValid
         }
 
         /// <inheritdoc/>
-        public virtual IUnivariateDistribution MarginalDistributionX { get; set; }
+        public virtual IUnivariateDistribution MarginalDistributionX { get; set; } = null!;
 
         /// <inheritdoc/>
-        public virtual IUnivariateDistribution MarginalDistributionY { get; set; }
+        public virtual IUnivariateDistribution MarginalDistributionY { get; set; } = null!;
 
         /// <inheritdoc/>
         public abstract string DisplayName { get; }
diff --git a/Numerics/Distributions/Bivariate Copulas/FrankCopula.cs b/Numerics/Distributions/Bivariate Copulas/FrankCopula.cs
index 6e0185fa..0b6a06a6 100644
--- a/Numerics/Distributions/Bivariate Copulas/FrankCopula.cs	
+++ b/Numerics/Distributions/Bivariate Copulas/FrankCopula.cs	
@@ -121,7 +121,7 @@ public override ArgumentOutOfRangeException ValidateParameter(double parameter,
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(Theta), "The dependency parameter θ (theta) must be less than or equal to " + ThetaMaximum.ToString() + ".");
                 return new ArgumentOutOfRangeException(nameof(Theta), "The dependency parameter θ (theta) must be less than or equal to " + ThetaMaximum.ToString() + ".");
             }
-            return null;
+            return new ArgumentOutOfRangeException(nameof(Theta),"Parameter is valid.");
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Bivariate Copulas/NormalCopula.cs b/Numerics/Distributions/Bivariate Copulas/NormalCopula.cs
index 4eaaf0c0..7215ba75 100644
--- a/Numerics/Distributions/Bivariate Copulas/NormalCopula.cs	
+++ b/Numerics/Distributions/Bivariate Copulas/NormalCopula.cs	
@@ -137,7 +137,7 @@ public override ArgumentOutOfRangeException ValidateParameter(double parameter,
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(Theta), "The correlation parameter ρ (rho) must be less than " + ThetaMaximum.ToString() + ".");
                 return new ArgumentOutOfRangeException(nameof(Theta), "The correlation parameter ρ (rho) must be less than " + ThetaMaximum.ToString() + ".");
             }
-            return null;
+            return new ArgumentOutOfRangeException(nameof(Theta),"The parameter is valid.");
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Multivariate/BivariateEmpirical.cs b/Numerics/Distributions/Multivariate/BivariateEmpirical.cs
index 8b334842..7a6e1d63 100644
--- a/Numerics/Distributions/Multivariate/BivariateEmpirical.cs
+++ b/Numerics/Distributions/Multivariate/BivariateEmpirical.cs
@@ -60,6 +60,9 @@ public BivariateEmpirical(Transform x1Transform = Transform.None, Transform x2Tr
             X1Transform = x1Transform;
             X2Transform = x2Transform;
             ProbabilityTransform = probabilityTransform;
+            X1Values = Array.Empty<double>();
+            X2Values = Array.Empty<double>();
+            ProbabilityValues = new double[0, 0];
         }
 
         /// <summary>
@@ -84,26 +87,26 @@ public BivariateEmpirical(IList<double> x1Values, IList<double> x2Values, double
         // ...
         // X1n P(n,1)   P(n,n)        
 
-        private Bilinear bilinear = null;
+        private Bilinear? bilinear = null;
         private bool _parametersValid = true;
 
         /// <summary>
         /// Return the array of X1 values (distribution 1). Points On the cumulative curve are specified
         /// with increasing value and increasing probability.
         /// </summary>
-        public double[] X1Values { get; private set; }
+        public double[] X1Values { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// Return the array of X2 values (distribution 2). Points on the cumulative curve are specified
         /// with increasing value and increasing probability.
         /// </summary>
-        public double[] X2Values { get; private set; }
+        public double[] X2Values { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// Return the array of probability values. Points on the cumulative curve are specified
         /// with increasing value and increasing probability.
         /// </summary>
-        public double[,] ProbabilityValues { get; private set; }
+        public double[,] ProbabilityValues { get; private set; } = new double[0, 0];
 
         /// <summary>
         /// Determines the interpolation transform for the X1-values.
@@ -183,7 +186,7 @@ public void SetParameters(IList<double> x1Values, IList<double> x2Values, double
         /// <param name="x2Values">Array of X2 values. The X2-values represent the secondary values. There are columns in the table of probability values.</param>
         /// <param name="pValues">Array of probability values. Range 0 ≤ p ≤ 1.</param>
         /// <param name="throwException">Determines whether to throw an exception or not.</param>
-        public ArgumentOutOfRangeException ValidateParameters(IList<double> x1Values, IList<double> x2Values, double[,] pValues, bool throwException)
+        public ArgumentOutOfRangeException? ValidateParameters(IList<double> x1Values, IList<double> x2Values, double[,] pValues, bool throwException)
         {
 
             if (x1Values.Count < 2)
diff --git a/Numerics/Distributions/Multivariate/MultivariateNormal.cs b/Numerics/Distributions/Multivariate/MultivariateNormal.cs
index 841a95d0..dbdcad75 100644
--- a/Numerics/Distributions/Multivariate/MultivariateNormal.cs
+++ b/Numerics/Distributions/Multivariate/MultivariateNormal.cs
@@ -89,24 +89,24 @@ public MultivariateNormal(double[] mean, double[,] covariance)
 
         private bool _parametersValid = true;
         private int _dimension = 0;
-        private double[] _mean;
-        private Matrix _covariance;
+        private double[] _mean = Array.Empty<double>();
+        private Matrix _covariance = Matrix.Identity(0);
         
-        private CholeskyDecomposition _cholesky;
+        private CholeskyDecomposition? _cholesky;
         private double _lnconstant;
-        private double[] _variance;
-        private double[] _standardDeviation;
+        private double[] _variance = Array.Empty<double>();
+        private double[] _standardDeviation = Array.Empty<double>();
 
         // variables required for the multivariate CDF
-        private Matrix _correlation;
-        private double[] _correl;
+        private Matrix _correlation = Matrix.Identity(0);
+        private double[] _correl = Array.Empty<double>();
         private Random _MVNUNI = new MersenneTwister();
         private int _maxEvaluations = 100000;
         private double _absoluteError = 1E-4;
         private double _relativeError = 1E-4;
-        private double[] _lower;
-        private double[] _upper;
-        private int[] _infin;
+        private double[] _lower = Array.Empty<double>();    
+        private double[] _upper = Array.Empty<double>();
+        private int[] _infin = Array.Empty<int>();
         private bool _correlationMatrixCreated = false;
         private bool _covSRTed = false;
 
@@ -250,7 +250,9 @@ public double[] StandardDeviation
         /// <summary>
         /// Determines if the covariance matrix is positive definite.
         /// </summary>
-        public bool IsPositiveDefinite => _cholesky.IsPositiveDefinite;
+        /// var chol = _cholesky ?? throw new InvalidOperationException("Parameters not set.");
+
+        public bool IsPositiveDefinite => _cholesky != null && _cholesky.IsPositiveDefinite;
 
         /// <summary>
         /// Set the distribution parameters.
@@ -265,6 +267,16 @@ public void SetParameters(double[] mean, double[,] covariance)
             _dimension = mean.Length;      
             _mean = mean;
             _covariance = new Matrix(covariance);
+
+            _variance = new double[_dimension];
+            _standardDeviation = new double[_dimension];
+            for (int i = 0; i < _dimension; i++)
+            {
+                // assuming Matrix supports indexer [row,col]
+                _variance[i] = _covariance[i, i];
+                _standardDeviation[i] = Math.Sqrt(_variance[i]);
+            }
+
             _cholesky = new CholeskyDecomposition(_covariance);
             double lndet = _cholesky.LogDeterminant();
             _lnconstant = -(Math.Log(2d * Math.PI) * _mean.Length + lndet) * 0.5d;
@@ -349,7 +361,7 @@ public ArgumentOutOfRangeException ValidateParameters(double[] mean, double[,] c
                 var ex = new ArgumentOutOfRangeException(nameof(Covariance), "Covariance matrix is not positive-definite.");
                 if (throwException) throw ex; else return ex;
             }
-            return null;
+            return null!;
         }
 
         /// <summary>
@@ -384,6 +396,8 @@ public double Mahalanobis(double[] x)
             var z = new double[_mean.Length];
             for (int i = 0; i < x.Length; i++)
                 z[i] = x[i] - _mean[i];
+            if(_cholesky == null)
+                throw new InvalidOperationException("Parameters not set.");
             var a = _cholesky.Solve(new Vector(z));
             double b = 0d;
             for (int i = 0; i < z.Length; i++)
@@ -475,7 +489,9 @@ public double[] InverseCDF(double[] probabilities)
             var z = new double[Dimension];
             for (int j = 0; j < Dimension; j++)
                 z[j] = Normal.StandardZ(probabilities[j]);
-            // x = A*z + mu
+            
+            if (_cholesky == null)
+                throw new InvalidOperationException("Parameters not set.");
             var Az = _cholesky.L * z;
             for (int j = 0; j < Dimension; j++)
                 sample[j] = Az[j] + _mean[j];
@@ -538,6 +554,8 @@ public static MultivariateNormal Bivariate(double mu1, double mu2, double sigma1
                 for (int j = 0; j < Dimension; j++)
                     z[j] = Normal.StandardZ(rnd.NextDouble());
                 // x = A*z + mu
+                if (_cholesky == null)
+                    throw new InvalidOperationException("Parameters not set.");
                 var Az = _cholesky.L * z;
                 for (int j = 0; j < Dimension; j++)
                     sample[i, j] = Az[j] + _mean[j];
@@ -566,6 +584,8 @@ public static MultivariateNormal Bivariate(double mu1, double mu2, double sigma1
                 for (int j = 0; j < Dimension; j++)
                     z[j] = Normal.StandardZ(r[i, j]);
                 // x = A*z + mu
+                if(_cholesky == null)
+                    throw new InvalidOperationException("Parameters not set.");
                 var Az = _cholesky.L * z;
                 for (int j = 0; j < Dimension; j++)
                     sample[i, j] = Az[j] + _mean[j];
@@ -601,6 +621,8 @@ public static MultivariateNormal Bivariate(double mu1, double mu2, double sigma1
                     }               
                 }               
                 // x = A*z + mu
+                if(_cholesky == null)
+                    throw new InvalidOperationException("Parameters not set.");
                 var Az = _cholesky.L * z;
                 for (int j = 0; j < Dimension; j++)
                     sample[i, j] = Az[j] + _mean[j];
diff --git a/Numerics/Distributions/Univariate/Base/UnivariateDistributionBase.cs b/Numerics/Distributions/Univariate/Base/UnivariateDistributionBase.cs
index 1a5cc312..08b96017 100644
--- a/Numerics/Distributions/Univariate/Base/UnivariateDistributionBase.cs
+++ b/Numerics/Distributions/Univariate/Base/UnivariateDistributionBase.cs
@@ -816,7 +816,7 @@ public virtual XElement ToXElement()
         /// </summary>
         /// <param name="obj">The object to compare with the current object.</param>
         /// <returns>True if the specified object is equal to the current object; otherwise, False.</returns>
-        public override bool Equals(object obj)
+        public override bool Equals(object? obj)
         {
             if (obj is UnivariateDistributionBase other)
             {
@@ -839,7 +839,7 @@ public override int GetHashCode()
                 if (Type == UnivariateDistributionType.Empirical)
                 {
                     var empirical = this as EmpiricalDistribution;
-                    if (empirical != null)
+                    if (empirical is not null)
                     {
                         foreach (var x in empirical.XValues)
                         {
@@ -871,9 +871,9 @@ public override int GetHashCode()
         /// <returns>
         /// A negative number if this instance's mean is less than the other; zero if equal; a positive number if greater.
         /// </returns>
-        public int CompareTo(UnivariateDistributionBase other)
+        public int CompareTo(UnivariateDistributionBase? other)
         {
-            if (other == null)
+            if (other is null)
                 return 1; // non-null instance is considered greater than null
 
             return this.Mean.CompareTo(other.Mean);
diff --git a/Numerics/Distributions/Univariate/Base/UnivariateDistributionFactory.cs b/Numerics/Distributions/Univariate/Base/UnivariateDistributionFactory.cs
index 91a79eab..84570380 100644
--- a/Numerics/Distributions/Univariate/Base/UnivariateDistributionFactory.cs
+++ b/Numerics/Distributions/Univariate/Base/UnivariateDistributionFactory.cs
@@ -204,7 +204,10 @@ public static UnivariateDistributionBase CreateDistribution(UnivariateDistributi
             {
                 distribution = new Weibull();
             }
-
+            if (distribution is null)
+            {
+                throw new ArgumentException("Distribution is not found.");
+            }
             return distribution;
         }
 
@@ -215,13 +218,14 @@ public static UnivariateDistributionBase CreateDistribution(UnivariateDistributi
         /// <returns>
         /// A univariate distribution.
         /// </returns>
-        public static UnivariateDistributionBase CreateDistribution(XElement xElement)
+        public static UnivariateDistributionBase? CreateDistribution(XElement xElement)
         {
             UnivariateDistributionType type;
-            UnivariateDistributionBase dist = null;
-            if (xElement.Attribute(nameof(UnivariateDistributionBase.Type)) != null)
+            UnivariateDistributionBase? dist = null;
+            var xAttr = xElement.Attribute(nameof(UnivariateDistributionBase.Type));
+            if (xAttr != null)
             {
-                Enum.TryParse(xElement.Attribute(nameof(UnivariateDistributionBase.Type)).Value, out type);
+                Enum.TryParse(xAttr.Value, out type);
 
                 if (type == UnivariateDistributionType.Mixture)
                 {
@@ -249,14 +253,19 @@ public static UnivariateDistributionBase CreateDistribution(XElement xElement)
                 }
 
             }
+            if (dist is null)
+            {
+                throw new ArgumentException("Distribution is not found.");
+            }
             var names = dist.GetParameterPropertyNames;
             var parms = dist.GetParameters;
             var vals = new double[dist.NumberOfParameters];
             for (int i = 0; i < dist.NumberOfParameters; i++)
             {
-                if (xElement.Attribute(names[i]) != null)
+                var xAttrParm = xElement.Attribute(names[i]);
+                if (xAttrParm != null)
                 {
-                    double.TryParse(xElement.Attribute(names[i]).Value, System.Globalization.NumberStyles.Any, System.Globalization.CultureInfo.InvariantCulture, out vals[i]);
+                    double.TryParse(xAttrParm.Value, System.Globalization.NumberStyles.Any, System.Globalization.CultureInfo.InvariantCulture, out vals[i]);
                 }
             }
             dist.SetParameters(vals);
diff --git a/Numerics/Distributions/Univariate/Bernoulli.cs b/Numerics/Distributions/Univariate/Bernoulli.cs
index 68ed1473..72d9268e 100644
--- a/Numerics/Distributions/Univariate/Bernoulli.cs
+++ b/Numerics/Distributions/Univariate/Bernoulli.cs
@@ -225,7 +225,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Probability), "Probability must be between 0 and 1.");
                 return new ArgumentOutOfRangeException(nameof(Probability), "Probability must be between 0 and 1.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/BetaDistribution.cs b/Numerics/Distributions/Univariate/BetaDistribution.cs
index f09d1ba3..6cb56ba6 100644
--- a/Numerics/Distributions/Univariate/BetaDistribution.cs
+++ b/Numerics/Distributions/Univariate/BetaDistribution.cs
@@ -255,7 +255,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                 return new ArgumentOutOfRangeException(nameof(Beta), "The shape parameter β (beta) must be positive.");
             }
             // 
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Binomial.cs b/Numerics/Distributions/Univariate/Binomial.cs
index baa238d7..9ffc70cd 100644
--- a/Numerics/Distributions/Univariate/Binomial.cs
+++ b/Numerics/Distributions/Univariate/Binomial.cs
@@ -256,7 +256,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(ProbabilityOfSuccess), "The number of trials (n) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(ProbabilityOfSuccess), "The number of trials (n) must be positive.");
             }
-            return null;
+            return null!;            
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Cauchy.cs b/Numerics/Distributions/Univariate/Cauchy.cs
index 836784b1..639c725a 100644
--- a/Numerics/Distributions/Univariate/Cauchy.cs
+++ b/Numerics/Distributions/Univariate/Cauchy.cs
@@ -230,7 +230,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Gamma), "The scale parameter γ (gamma) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Gamma), "The scale parameter γ (gamma) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/ChiSquared.cs b/Numerics/Distributions/Univariate/ChiSquared.cs
index 6a080b89..1f6cdaa7 100644
--- a/Numerics/Distributions/Univariate/ChiSquared.cs
+++ b/Numerics/Distributions/Univariate/ChiSquared.cs
@@ -242,7 +242,7 @@ public ArgumentOutOfRangeException ValidateParameters(int degreesOfFreedom, bool
                 return new ArgumentOutOfRangeException(nameof(degreesOfFreedom), "The degrees of freedom ν (nu) must greater than or equal to one.");
             }
 
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/CompetingRisks.cs b/Numerics/Distributions/Univariate/CompetingRisks.cs
index 8a713821..e0ec9b98 100644
--- a/Numerics/Distributions/Univariate/CompetingRisks.cs
+++ b/Numerics/Distributions/Univariate/CompetingRisks.cs
@@ -79,14 +79,14 @@ public CompetingRisks(IUnivariateDistribution[] distributions)
             SetParameters(distributions);
         }
 
-        private UnivariateDistributionBase[] _distributions;
-        private EmpiricalDistribution _empiricalCDF;
+        private UnivariateDistributionBase[] _distributions = Array.Empty<UnivariateDistributionBase>();
+        private EmpiricalDistribution _empiricalCDF = null!;
         private bool _momentsComputed = false;
         private double u1, u2, u3, u4;
         private bool _empiricalCDFCreated = false;
-        private double[,] _correlationMatrix;
+        private double[,] _correlationMatrix = null!;
         private bool _mvnCreated = false;
-        private MultivariateNormal _mvn;
+        private MultivariateNormal _mvn = null!;
 
         /// <summary>
         /// Returns the array of univariate probability distributions.
@@ -422,7 +422,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     return new ArgumentOutOfRangeException(nameof(Distributions), "One of the distributions have invalid parameters.");
                 }
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
@@ -620,7 +620,7 @@ public override double InverseCDF(double probability)
         /// Returns a list of cumulative incidence functions. 
         /// </summary>
         /// <param name="bins">Optional. The stratification bins to integrate over. Default is 200 bins.</param>
-        public List<EmpiricalDistribution> CumulativeIncidenceFunctions(List<StratificationBin> bins = null)
+        public List<EmpiricalDistribution> CumulativeIncidenceFunctions(List<StratificationBin>? bins = null)
         {
             // Get stratification bins
             if (bins == null)
@@ -928,7 +928,7 @@ public override UnivariateDistributionBase Clone()
                 ProbabilityTransform = ProbabilityTransform
             };
             if (CorrelationMatrix != null)
-                cr.CorrelationMatrix = CorrelationMatrix.Clone() as double[,];
+                cr.CorrelationMatrix = (double[,]) CorrelationMatrix.Clone();
 
             return cr;
         }
@@ -989,20 +989,22 @@ public override XElement ToXElement()
         /// </summary>
         /// <param name="xElement">The XElement to deserialize.</param>
         /// <returns>A new competing risks distribution.</returns>
-        public static CompetingRisks FromXElement(XElement xElement)
+        public static CompetingRisks? FromXElement(XElement xElement)
         {
             UnivariateDistributionType type = UnivariateDistributionType.Deterministic;
-            if (xElement.Attribute(nameof(UnivariateDistributionBase.Type)) != null)
+            var xElAttr = xElement.Attribute(nameof(UnivariateDistributionBase.Type));
+            if (xElAttr != null)
             {
-                Enum.TryParse(xElement.Attribute(nameof(UnivariateDistributionBase.Type)).Value, out type);
+                Enum.TryParse(xElAttr.Value, out type);
 
             }
             if (type == UnivariateDistributionType.CompetingRisks)
             {
                 var distributions = new List<UnivariateDistributionBase>();
-                if (xElement.Attribute(nameof(Distributions)) != null)
+                var xDistAttr = xElement.Attribute(nameof(Distributions));
+                if (xDistAttr != null)
                 {
-                    var types = xElement.Attribute(nameof(Distributions)).Value.Split('|');
+                    var types = xDistAttr.Value.Split('|');
                     for (int i = 0; i < types.Length; i++)
                     {
                         Enum.TryParse(types[i], out UnivariateDistributionType distType);
@@ -1013,29 +1015,46 @@ public static CompetingRisks FromXElement(XElement xElement)
 
                 if (xElement.Attribute(nameof(XTransform)) != null)
                 {
-                    Enum.TryParse(xElement.Attribute(nameof(XTransform)).Value, out Transform xTransform);
-                    competingRisks.XTransform = xTransform;
+                    var xTransformAttr = xElement.Attribute(nameof(XTransform));
+                    if (xTransformAttr != null)
+                    {
+                        Enum.TryParse(xTransformAttr.Value, out Transform xTransform);
+                        competingRisks.XTransform = xTransform;
+                    }
                 }
                 if (xElement.Attribute(nameof(ProbabilityTransform)) != null)
                 {
-                    Enum.TryParse(xElement.Attribute(nameof(ProbabilityTransform)).Value, out Transform probabilityTransform);
-                    competingRisks.ProbabilityTransform = probabilityTransform;
+                    var xProbabilityAttr = xElement.Attribute(nameof(ProbabilityTransform));
+                    if (xProbabilityAttr != null)
+                    {
+                        Enum.TryParse(xProbabilityAttr.Value, out Transform probabilityTransform);
+                        competingRisks.ProbabilityTransform = probabilityTransform;
+                    }
                 }
                 if (xElement.Attribute(nameof(MinimumOfRandomVariables)) != null)
                 {
-                    bool.TryParse(xElement.Attribute(nameof(MinimumOfRandomVariables)).Value, out bool minOfValues);
-                    competingRisks.MinimumOfRandomVariables = minOfValues;
+                    var xMinOfAttr = xElement.Attribute(nameof(MinimumOfRandomVariables));
+                    if (xMinOfAttr != null)
+                    {
+                        bool.TryParse(xMinOfAttr.Value, out bool minOfValues);
+                        competingRisks.MinimumOfRandomVariables = minOfValues;
+                    }
                 }
                 if (xElement.Attribute(nameof(Dependency)) != null)
                 {
-                    Enum.TryParse(xElement.Attribute(nameof(Dependency)).Value, out Probability.DependencyType dependency);
-                    competingRisks.Dependency = dependency;
+                    var xDependencyAttr = xElement.Attribute(nameof(Dependency));
+                    if (xDependencyAttr != null)
+                    {
+                        Enum.TryParse(xDependencyAttr.Value, out Probability.DependencyType dependency);
+                        competingRisks.Dependency = dependency;
+                    }
                 }
 
                 // Parameters
-                if (xElement.Attribute("Parameters") != null)
-                {
-                    var vals = xElement.Attribute("Parameters").Value.Split('|');
+                var xParametersAttr = xElement.Attribute("Parameters");
+                if (xParametersAttr != null)
+                {   
+                    var vals = xParametersAttr.Value.Split('|');
                     var parameters = new List<double>();
                     for (int i = 0; i < vals.Length; i++)
                     {
diff --git a/Numerics/Distributions/Univariate/Deterministic.cs b/Numerics/Distributions/Univariate/Deterministic.cs
index 77bd7da7..99cdad1e 100644
--- a/Numerics/Distributions/Univariate/Deterministic.cs
+++ b/Numerics/Distributions/Univariate/Deterministic.cs
@@ -238,7 +238,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Probability), "The point value must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Probability), "The point value must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/EmpiricalDistribution.cs b/Numerics/Distributions/Univariate/EmpiricalDistribution.cs
index 4627d6dc..af05b7e9 100644
--- a/Numerics/Distributions/Univariate/EmpiricalDistribution.cs
+++ b/Numerics/Distributions/Univariate/EmpiricalDistribution.cs
@@ -155,14 +155,19 @@ public EmpiricalDistribution(IList<double> sample, PlottingPositions.PlottingPos
         {
             _xValues = sample.ToArray();
             Array.Sort(_xValues);
-            _pValues = PlottingPositions.Function(_xValues.Count(), plottingPostionType);
+            
+            var pValues = PlottingPositions.Function(_xValues.Count(), plottingPostionType);
+
+            if (pValues is null) { throw new InvalidOperationException("PlottingPositions.Function returned null."); }
+            _pValues = pValues;
+
             opd = new OrderedPairedData(_xValues, _pValues, true, SortOrder.Ascending, true, SortOrder.Ascending);
             _momentsComputed = false;
         }
 
-        private double[] _xValues;
-        private double[] _pValues;
-        private OrderedPairedData opd;
+        private double[] _xValues = Array.Empty<double>();
+        private double[] _pValues = Array.Empty<double>();
+        private OrderedPairedData opd = default!;
         private bool _momentsComputed = false;
         private double u1, u2, u3, u4;
 
@@ -407,7 +412,7 @@ public override void SetParameters(IList<double> parameters)
         /// <inheritdoc/>
         public override ArgumentOutOfRangeException ValidateParameters(IList<double> parameters, bool throwException)
         {
-            return null;
+            return new ArgumentOutOfRangeException("The parameters are valid");
         }
 
         /// <inheritdoc/>
@@ -699,7 +704,7 @@ public static EmpiricalDistribution Convolve(IList<EmpiricalDistribution> distri
                 throw new ArgumentException("Distribution list cannot be null or empty.", nameof(distributions));
 
             if (distributions.Count == 1)
-                return distributions[0].Clone() as EmpiricalDistribution;
+                return (EmpiricalDistribution) distributions[0].Clone();
 
             if (numberOfPoints < 2)
                 throw new ArgumentException("Number of points must be at least 2.", nameof(numberOfPoints));
diff --git a/Numerics/Distributions/Univariate/Exponential.cs b/Numerics/Distributions/Univariate/Exponential.cs
index 5dbc109d..7bf019d1 100644
--- a/Numerics/Distributions/Univariate/Exponential.cs
+++ b/Numerics/Distributions/Univariate/Exponential.cs
@@ -285,7 +285,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Alpha), "The scale parameter α (alpha) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Alpha), "The scale parameter α (alpha) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/GammaDistribution.cs b/Numerics/Distributions/Univariate/GammaDistribution.cs
index 6e23df39..5b8419a0 100644
--- a/Numerics/Distributions/Univariate/GammaDistribution.cs
+++ b/Numerics/Distributions/Univariate/GammaDistribution.cs
@@ -389,7 +389,7 @@ public ArgumentOutOfRangeException ValidateParameters(double scale, double shape
                     throw new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/GeneralizedBeta.cs b/Numerics/Distributions/Univariate/GeneralizedBeta.cs
index 2c569aa6..24dd2472 100644
--- a/Numerics/Distributions/Univariate/GeneralizedBeta.cs
+++ b/Numerics/Distributions/Univariate/GeneralizedBeta.cs
@@ -399,7 +399,7 @@ public ArgumentOutOfRangeException ValidateParameters(double alpha, double beta,
                     throw new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than or equal to the max.");
                 return new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than or equal to the max.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/GeneralizedExtremeValue.cs b/Numerics/Distributions/Univariate/GeneralizedExtremeValue.cs
index 18dd7c54..3d89127e 100644
--- a/Numerics/Distributions/Univariate/GeneralizedExtremeValue.cs
+++ b/Numerics/Distributions/Univariate/GeneralizedExtremeValue.cs
@@ -420,7 +420,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/GeneralizedLogistic.cs b/Numerics/Distributions/Univariate/GeneralizedLogistic.cs
index 0b4c9b4c..81093b4a 100644
--- a/Numerics/Distributions/Univariate/GeneralizedLogistic.cs
+++ b/Numerics/Distributions/Univariate/GeneralizedLogistic.cs
@@ -412,7 +412,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/GeneralizedNormal.cs b/Numerics/Distributions/Univariate/GeneralizedNormal.cs
index 2ac643d8..b6888930 100644
--- a/Numerics/Distributions/Univariate/GeneralizedNormal.cs
+++ b/Numerics/Distributions/Univariate/GeneralizedNormal.cs
@@ -368,7 +368,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/GeneralizedPareto.cs b/Numerics/Distributions/Univariate/GeneralizedPareto.cs
index 220a3c0a..bcdb1fac 100644
--- a/Numerics/Distributions/Univariate/GeneralizedPareto.cs
+++ b/Numerics/Distributions/Univariate/GeneralizedPareto.cs
@@ -412,7 +412,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Geometric.cs b/Numerics/Distributions/Univariate/Geometric.cs
index 1ee2b39e..7dbe5b60 100644
--- a/Numerics/Distributions/Univariate/Geometric.cs
+++ b/Numerics/Distributions/Univariate/Geometric.cs
@@ -219,7 +219,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(ProbabilityOfSuccess), "Probability must be between 0 and 1.");
                 return new ArgumentOutOfRangeException(nameof(ProbabilityOfSuccess), "Probability must be between 0 and 1.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Gumbel.cs b/Numerics/Distributions/Univariate/Gumbel.cs
index 2c08a36f..97b1b428 100644
--- a/Numerics/Distributions/Univariate/Gumbel.cs
+++ b/Numerics/Distributions/Univariate/Gumbel.cs
@@ -290,7 +290,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(Alpha), "The scale parameter α (alpha) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Alpha), "The scale parameter α (alpha) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/InverseChiSquared.cs b/Numerics/Distributions/Univariate/InverseChiSquared.cs
index 859afef1..433e2b41 100644
--- a/Numerics/Distributions/Univariate/InverseChiSquared.cs
+++ b/Numerics/Distributions/Univariate/InverseChiSquared.cs
@@ -269,7 +269,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "The scale parameter σ (sigma) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "The scale parameter σ (sigma) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/InverseGamma.cs b/Numerics/Distributions/Univariate/InverseGamma.cs
index f2de885c..778b5a65 100644
--- a/Numerics/Distributions/Univariate/InverseGamma.cs
+++ b/Numerics/Distributions/Univariate/InverseGamma.cs
@@ -255,7 +255,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Alpha), "The shape parameter α (alpha) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Alpha), "The shape parameter α (alpha) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/KappaFour.cs b/Numerics/Distributions/Univariate/KappaFour.cs
index a49a1350..390b6306 100644
--- a/Numerics/Distributions/Univariate/KappaFour.cs
+++ b/Numerics/Distributions/Univariate/KappaFour.cs
@@ -405,7 +405,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Hondo), "The shape parameter h (hondo) must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Hondo), "The shape parameter h (hondo) must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/KernelDensity.cs b/Numerics/Distributions/Univariate/KernelDensity.cs
index b837afab..7cc8c808 100644
--- a/Numerics/Distributions/Univariate/KernelDensity.cs
+++ b/Numerics/Distributions/Univariate/KernelDensity.cs
@@ -152,14 +152,14 @@ public enum KernelType
             Uniform
         }
 
-        private double[] _sampleData;
+        private double[] _sampleData = Array.Empty<double>();
         private double _bandwidth;
         private KernelType _kernelDistribution;
-        private IKernel _kernel;
+        private IKernel _kernel = null!;
         private bool _cdfCreated = false;
-        private OrderedPairedData opd;
+        private OrderedPairedData opd = null!;
         private double u1, u2, u3, u4;
-        private double[] _weights;     // one weight per sample (unnormalised)
+        private double[] _weights = null!;     // one weight per sample (unnormalised)
         private double _sumW = 1.0;  // Σ wᵢ   (defaults to 1 for un‑weighted case)
 
 
@@ -549,7 +549,7 @@ public double BandwidthRule(IList<double> sampleData)
         /// </summary>
         /// <param name="sample">Sample of data, no sorting is assumed.</param>
         /// <param name="w">A list of weights.</param>
-        public double BandwidthRule(IList<double> sample, IList<double> w = null)
+        public double BandwidthRule(IList<double> sample, IList<double> w = null!)
         {
             w ??= Enumerable.Repeat(1.0, sample.Count).ToArray();
             double m = w.Zip(sample, (wi, xi) => wi * xi).Sum() / w.Sum();
@@ -574,7 +574,7 @@ public override void SetParameters(IList<double> parameters)
         /// <inheritdoc/>
         public override ArgumentOutOfRangeException ValidateParameters(IList<double> parameters, bool throwException)
         {
-            return null;
+            return null!;
         }
 
         /// <summary>
@@ -588,7 +588,7 @@ private ArgumentOutOfRangeException ValidateParameters(double value, bool throwE
                     throw new ArgumentOutOfRangeException(nameof(Bandwidth), "The bandwidth must be a positive number!");
                 return new ArgumentOutOfRangeException(nameof(Bandwidth), "The bandwidth must be a positive number!");
             }
-            return null;
+            return null!;
         }
 
         /// <summary>
diff --git a/Numerics/Distributions/Univariate/LnNormal.cs b/Numerics/Distributions/Univariate/LnNormal.cs
index 8956f4d9..9bd42ce4 100644
--- a/Numerics/Distributions/Univariate/LnNormal.cs
+++ b/Numerics/Distributions/Univariate/LnNormal.cs
@@ -311,7 +311,7 @@ public ArgumentOutOfRangeException ValidateParameters(double mean, double standa
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "Sigma must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "Sigma must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/LogNormal.cs b/Numerics/Distributions/Univariate/LogNormal.cs
index d341a8a4..f2829d76 100644
--- a/Numerics/Distributions/Univariate/LogNormal.cs
+++ b/Numerics/Distributions/Univariate/LogNormal.cs
@@ -365,7 +365,7 @@ public ArgumentOutOfRangeException ValidateParameters(double mu, double sigma, b
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "Sigma must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "Sigma must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/LogPearsonTypeIII.cs b/Numerics/Distributions/Univariate/LogPearsonTypeIII.cs
index afea3dc3..bd2ffdd1 100644
--- a/Numerics/Distributions/Univariate/LogPearsonTypeIII.cs
+++ b/Numerics/Distributions/Univariate/LogPearsonTypeIII.cs
@@ -529,7 +529,7 @@ public ArgumentOutOfRangeException ValidateParameters(double mu, double sigma, d
                     throw new ArgumentOutOfRangeException(nameof(Gamma), "Gamma = " + gamma + ". Gamma must be greater than -5.");
                 return new ArgumentOutOfRangeException(nameof(Gamma), "Gamma = " + gamma + ". Gamma must be greater than -5.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Logistic.cs b/Numerics/Distributions/Univariate/Logistic.cs
index a06db19d..9b934d01 100644
--- a/Numerics/Distributions/Univariate/Logistic.cs
+++ b/Numerics/Distributions/Univariate/Logistic.cs
@@ -281,7 +281,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Alpha), "The scale parameter α (alpha) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Alpha), "The scale parameter α (alpha) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Mixture.cs b/Numerics/Distributions/Univariate/Mixture.cs
index 5472f70b..a782d856 100644
--- a/Numerics/Distributions/Univariate/Mixture.cs
+++ b/Numerics/Distributions/Univariate/Mixture.cs
@@ -79,9 +79,9 @@ public Mixture(double[] weights, IUnivariateDistribution[] distributions)
             SetParameters(weights, distributions);
         }
 
-        private double[] _weights;
-        private UnivariateDistributionBase[] _distributions;
-        private EmpiricalDistribution _empiricalCDF;
+        private double[] _weights = Array.Empty<double>();
+        private UnivariateDistributionBase[] _distributions = null!;
+        private EmpiricalDistribution _empiricalCDF = null!;
         private bool _momentsComputed = false;
         private double u1, u2, u3, u4;
         private bool _empiricalCDFCreated = false;
@@ -618,7 +618,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     return new ArgumentOutOfRangeException(nameof(Distributions), "Distribution " + (i + 1).ToString() + " has invalid parameters.");
                 }
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
@@ -1086,27 +1086,30 @@ public override XElement ToXElement()
         public static Mixture FromXElement(XElement xElement)
         {
             UnivariateDistributionType type = UnivariateDistributionType.Deterministic;
-            if (xElement.Attribute(nameof(UnivariateDistributionBase.Type)) != null)
+            var univBaseAttr = xElement.Attribute(nameof(UnivariateDistributionBase.Type));
+            if (univBaseAttr != null)
             {
-                Enum.TryParse(xElement.Attribute(nameof(UnivariateDistributionBase.Type)).Value, out type);
+                Enum.TryParse(univBaseAttr.Value, out type);
 
             }
             if (type == UnivariateDistributionType.Mixture)
             {
                 var weights = new List<double>();
                 var distributions = new List<UnivariateDistributionBase>();
-                if (xElement.Attribute(nameof(Weights)) != null)
+                var weightAttr = xElement.Attribute(nameof(Weights));
+                if (weightAttr != null)
                 {
-                    var w = xElement.Attribute(nameof(Weights)).Value.Split('|');
+                    var w = weightAttr.Value.Split('|');
                     for (int i = 0; i < w.Length; i++)
                     {
                         double.TryParse(w[i], NumberStyles.Any, CultureInfo.InvariantCulture, out var weight);
                         weights.Add(weight);
                     }
                 }
-                if (xElement.Attribute(nameof(Distributions)) != null)
+                var distAttr = xElement.Attribute(nameof(Distributions));
+                if (distAttr != null)
                 {
-                    var types = xElement.Attribute(nameof(Distributions)).Value.Split('|');
+                    var types = distAttr.Value.Split('|');
                     for (int i = 0; i < types.Length; i++)
                     {
                         Enum.TryParse(types[i], out UnivariateDistributionType distType);
@@ -1114,30 +1117,34 @@ public static Mixture FromXElement(XElement xElement)
                     }
                 }
                 var mixture = new Mixture(weights.ToArray(), distributions.ToArray());
-
-                if (xElement.Attribute(nameof(IsZeroInflated)) != null)
+                var zeroInflatedAttr = xElement.Attribute(nameof(IsZeroInflated));
+                if (zeroInflatedAttr != null)
                 {
-                    bool.TryParse(xElement.Attribute(nameof(IsZeroInflated)).Value, out var isZeroInflated);
+                    bool.TryParse(zeroInflatedAttr.Value, out var isZeroInflated);
                     mixture.IsZeroInflated = isZeroInflated;
                 }
-                if (xElement.Attribute(nameof(ZeroWeight)) != null)
+                var zeroWeightAttr = xElement.Attribute(nameof(ZeroWeight));
+                if (zeroWeightAttr != null)
                 {
-                    double.TryParse(xElement.Attribute(nameof(ZeroWeight)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var zeroWeight);
+                    double.TryParse(zeroWeightAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var zeroWeight);
                     mixture.ZeroWeight = zeroWeight;
                 }
-                if (xElement.Attribute(nameof(XTransform)) != null)
+                var xTransformAttr = xElement.Attribute(nameof(XTransform));
+                if (xTransformAttr != null)
                 {
-                    Enum.TryParse(xElement.Attribute(nameof(XTransform)).Value, out Transform xTransform);
+                    Enum.TryParse(xTransformAttr.Value, out Transform xTransform);
                     mixture.XTransform = xTransform;
                 }
-                if (xElement.Attribute(nameof(ProbabilityTransform)) != null)
+                var xProbabilityTransformAttr = xElement.Attribute(nameof(ProbabilityTransform));
+                if (xProbabilityTransformAttr != null)
                 {
-                    Enum.TryParse(xElement.Attribute(nameof(ProbabilityTransform)).Value, out Transform probabilityTransform);
+                    Enum.TryParse(xProbabilityTransformAttr.Value, out Transform probabilityTransform);
                     mixture.ProbabilityTransform = probabilityTransform;
                 }
-                if (xElement.Attribute("Parameters") != null)
+                var xParametersAttr = xElement.Attribute("Parameters");
+                if (xParametersAttr != null)
                 {
-                    var vals = xElement.Attribute("Parameters").Value.Split('|');
+                    var vals = xParametersAttr.Value.Split('|');
                     var parameters = new List<double>();
                     for (int i = 0; i < vals.Length; i++)
                     {
@@ -1151,7 +1158,7 @@ public static Mixture FromXElement(XElement xElement)
             }
             else
             {
-                return null;
+                return null!;
             }
         }
 
diff --git a/Numerics/Distributions/Univariate/NoncentralT.cs b/Numerics/Distributions/Univariate/NoncentralT.cs
index e8b50077..e2b473fa 100644
--- a/Numerics/Distributions/Univariate/NoncentralT.cs
+++ b/Numerics/Distributions/Univariate/NoncentralT.cs
@@ -289,7 +289,7 @@ public ArgumentOutOfRangeException ValidateParameters(double v, double mu, bool
                     throw new ArgumentOutOfRangeException(nameof(Noncentrality), "The noncentrality parameter μ (mu) must be a number.");
                 return new ArgumentOutOfRangeException(nameof(Noncentrality), "The noncentrality parameter μ (mu) must be a number.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Normal.cs b/Numerics/Distributions/Univariate/Normal.cs
index f62911c2..5a993176 100644
--- a/Numerics/Distributions/Univariate/Normal.cs
+++ b/Numerics/Distributions/Univariate/Normal.cs
@@ -349,7 +349,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "Standard deviation must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "Standard deviation must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Pareto.cs b/Numerics/Distributions/Univariate/Pareto.cs
index 5caed988..e1e0a85e 100644
--- a/Numerics/Distributions/Univariate/Pareto.cs
+++ b/Numerics/Distributions/Univariate/Pareto.cs
@@ -261,7 +261,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Alpha), "The shape parameter α (alpha) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Alpha), "The shape parameter α (alpha) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/PearsonTypeIII.cs b/Numerics/Distributions/Univariate/PearsonTypeIII.cs
index 598bb216..74a67c67 100644
--- a/Numerics/Distributions/Univariate/PearsonTypeIII.cs
+++ b/Numerics/Distributions/Univariate/PearsonTypeIII.cs
@@ -406,7 +406,7 @@ public ArgumentOutOfRangeException ValidateParameters(double mu, double sigma, d
                     throw new ArgumentOutOfRangeException(nameof(Gamma), "Gamma = " + gamma + ". Gamma must be greater than -5.");
                 return new ArgumentOutOfRangeException(nameof(Gamma), "Gamma = " + gamma + ". Gamma must be greater than -5.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
@@ -814,7 +814,7 @@ public override double[] ConditionalMoments(double a, double b)
         private (bool stable, bool divisable) TryPearsonConditionalMoments(
             double a, double b, out double[] moments, double pMin)
         {
-            moments = null;
+            moments = null!;
 
             // ---- small math helpers -------------------------------------------------
             static double Phi(double x) => 0.5 * (1.0 + Mathematics.SpecialFunctions.Erf.Function(x / Math.Sqrt(2.0)));
diff --git a/Numerics/Distributions/Univariate/Pert.cs b/Numerics/Distributions/Univariate/Pert.cs
index e7c0e81c..62235836 100644
--- a/Numerics/Distributions/Univariate/Pert.cs
+++ b/Numerics/Distributions/Univariate/Pert.cs
@@ -340,7 +340,7 @@ private ArgumentOutOfRangeException ValidateParameters(double min, double mode,
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(MostLikely), "The mode (most likely) must be between the min and max.");
                 return new ArgumentOutOfRangeException(nameof(MostLikely), "The mode (most likely) must be between the min and max.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/PertPercentile.cs b/Numerics/Distributions/Univariate/PertPercentile.cs
index 24b4e4f4..08d2c83a 100644
--- a/Numerics/Distributions/Univariate/PertPercentile.cs
+++ b/Numerics/Distributions/Univariate/PertPercentile.cs
@@ -326,7 +326,7 @@ private ArgumentOutOfRangeException ValidateParameters(double fifth, double fift
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(Percentile50th), "The 50% must be between the 5% and 95%.");
                 return new ArgumentOutOfRangeException(nameof(Percentile50th), "The 50% must be between the 5% and 95%.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
@@ -507,17 +507,19 @@ public override XElement ToXElement()
         public static PertPercentile FromXElement(XElement xElement)
         {
             UnivariateDistributionType type = UnivariateDistributionType.Deterministic;
-            if (xElement.Attribute(nameof(UnivariateDistributionBase.Type)) != null)
+            var xUnivAttr = xElement.Attribute(nameof(UnivariateDistributionBase.Type));
+            if ( xUnivAttr != null)
             {
-                Enum.TryParse(xElement.Attribute(nameof(UnivariateDistributionBase.Type)).Value, out type);
+                Enum.TryParse(xUnivAttr.Value, out type);
 
             }
             if (type == UnivariateDistributionType.PertPercentile)
             {
                 bool parametersSolved = false;
-                if (xElement.Attribute("ParametersSolved") != null)
+                var xParamSolvedAttr = xElement.Attribute("ParametersSolved");
+                if (xParamSolvedAttr != null)
                 {
-                    bool.TryParse(xElement.Attribute("ParametersSolved").Value, out parametersSolved);
+                    bool.TryParse(xParamSolvedAttr.Value, out parametersSolved);
                 }
                 else
                 {
@@ -528,9 +530,10 @@ public static PertPercentile FromXElement(XElement xElement)
                     var vals = new double[dist.NumberOfParameters];
                     for (int i = 0; i < dist.NumberOfParameters; i++)
                     {
-                        if (xElement.Attribute(names[i]) != null)
+                        var xAttr = xElement.Attribute(names[i]);
+                        if (xAttr != null)
                         {
-                            double.TryParse(xElement.Attribute(names[i]).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out vals[i]);
+                            double.TryParse(xAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out vals[i]);
                         }
                     }
                     dist.SetParameters(vals);
@@ -539,9 +542,10 @@ public static PertPercentile FromXElement(XElement xElement)
                 }
 
                 var beta = new GeneralizedBeta();
-                if (xElement.Attribute("BetaParameters") != null)
+                var xBetaAttr = xElement.Attribute("BetaParameters");
+                if (xBetaAttr != null)
                 {
-                    var vals = xElement.Attribute("BetaParameters").Value.Split('|');
+                    var vals = xBetaAttr.Value.Split('|');
                     var parameters = new List<double>();
                     for (int i = 0; i < vals.Length; i++)
                     {
@@ -551,9 +555,10 @@ public static PertPercentile FromXElement(XElement xElement)
                     beta.SetParameters(parameters);
                 }
                 double _5th = 0, _50th = 0, _95th = 0;
-                if (xElement.Attribute("Parameters") != null)
+                var xParamAttr = xElement.Attribute("Parameters");
+                if (xParamAttr != null)
                 {
-                    var vals = xElement.Attribute("Parameters").Value.Split('|');
+                    var vals = xParamAttr.Value.Split('|');
                     double.TryParse(vals[0], NumberStyles.Any, CultureInfo.InvariantCulture, out _5th);
                     double.TryParse(vals[1], NumberStyles.Any, CultureInfo.InvariantCulture, out _50th);
                     double.TryParse(vals[2], NumberStyles.Any, CultureInfo.InvariantCulture, out _95th);
@@ -571,7 +576,7 @@ public static PertPercentile FromXElement(XElement xElement)
             }
             else
             {
-                return null;
+                return null!;
             }
         }
 
diff --git a/Numerics/Distributions/Univariate/PertPercentileZ.cs b/Numerics/Distributions/Univariate/PertPercentileZ.cs
index 554c3aae..b32bd60f 100644
--- a/Numerics/Distributions/Univariate/PertPercentileZ.cs
+++ b/Numerics/Distributions/Univariate/PertPercentileZ.cs
@@ -330,7 +330,7 @@ private ArgumentOutOfRangeException ValidateParameters(double fifth, double fift
                 if (throwException) throw new ArgumentOutOfRangeException(nameof(Percentile95th), "The percentiles must be between 0 and 1.");
                 return new ArgumentOutOfRangeException(nameof(Percentile95th), "The percentiles must be between 0 and 1.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
@@ -504,17 +504,19 @@ public override XElement ToXElement()
         public static PertPercentileZ FromXElement(XElement xElement)
         {
             UnivariateDistributionType type = UnivariateDistributionType.Deterministic;
-            if (xElement.Attribute(nameof(UnivariateDistributionBase.Type)) != null)
+            var xUnivAttr = xElement.Attribute(nameof(UnivariateDistributionBase.Type));
+            if ( xUnivAttr != null)
             {
-                Enum.TryParse(xElement.Attribute(nameof(UnivariateDistributionBase.Type)).Value, out type);
+                Enum.TryParse(xUnivAttr.Value, out type);
 
             }
             if (type == UnivariateDistributionType.PertPercentileZ)
             {
                 bool parametersSolved = false;
-                if (xElement.Attribute("ParametersSolved") != null)
+                var xParamSolvedAttr = xElement.Attribute("ParametersSolved");
+                if (xParamSolvedAttr != null)
                 {
-                    bool.TryParse(xElement.Attribute("ParametersSolved").Value, out parametersSolved);
+                    bool.TryParse(xParamSolvedAttr.Value, out parametersSolved);
                 }
                 else
                 {
@@ -525,9 +527,10 @@ public static PertPercentileZ FromXElement(XElement xElement)
                     var vals = new double[dist.NumberOfParameters];
                     for (int i = 0; i < dist.NumberOfParameters; i++)
                     {
-                        if (xElement.Attribute(names[i]) != null)
+                        var xAttr = xElement.Attribute(names[i]);
+                        if (xAttr != null)
                         {
-                            double.TryParse(xElement.Attribute(names[i]).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out vals[i]);
+                            double.TryParse(xAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out vals[i]);
                         }
                     }
                     dist.SetParameters(vals);
@@ -536,9 +539,10 @@ public static PertPercentileZ FromXElement(XElement xElement)
                 }
 
                 var beta = new GeneralizedBeta();
-                if (xElement.Attribute("BetaParameters") != null)
+                var xBetaAttr = xElement.Attribute("BetaParameters");
+                if (xBetaAttr != null)
                 {
-                    var vals = xElement.Attribute("BetaParameters").Value.Split('|');
+                    var vals = xBetaAttr.Value.Split('|');
                     var parameters = new List<double>();
                     for (int i = 0; i < vals.Length; i++)
                     {
@@ -548,9 +552,10 @@ public static PertPercentileZ FromXElement(XElement xElement)
                     beta.SetParameters(parameters);
                 }
                 double _5th = 0, _50th = 0, _95th = 0;
-                if (xElement.Attribute("Parameters") != null)
+                var xParamAttr = xElement.Attribute("Parameters");
+                if (xParamAttr != null)
                 {
-                    var vals = xElement.Attribute("Parameters").Value.Split('|');
+                    var vals = xParamAttr.Value.Split('|');
                     double.TryParse(vals[0], NumberStyles.Any, CultureInfo.InvariantCulture, out _5th);
                     double.TryParse(vals[1], NumberStyles.Any, CultureInfo.InvariantCulture, out _50th);
                     double.TryParse(vals[2], NumberStyles.Any, CultureInfo.InvariantCulture, out _95th);
@@ -569,7 +574,7 @@ public static PertPercentileZ FromXElement(XElement xElement)
             }
             else
             {
-                return null;
+                return null!;
             }
         }
 
diff --git a/Numerics/Distributions/Univariate/Poisson.cs b/Numerics/Distributions/Univariate/Poisson.cs
index 6295ddce..9e8d1821 100644
--- a/Numerics/Distributions/Univariate/Poisson.cs
+++ b/Numerics/Distributions/Univariate/Poisson.cs
@@ -217,7 +217,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Lambda), "The rate (λ) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Lambda), "The rate (λ) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Rayleigh.cs b/Numerics/Distributions/Univariate/Rayleigh.cs
index c5bca12c..8c74e25e 100644
--- a/Numerics/Distributions/Univariate/Rayleigh.cs
+++ b/Numerics/Distributions/Univariate/Rayleigh.cs
@@ -267,7 +267,7 @@ public ArgumentOutOfRangeException ValidateParameters(double scale, bool throwEx
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "Standard deviation must be greater than zero.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "Standard deviation must be greater than zero.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/StudentT.cs b/Numerics/Distributions/Univariate/StudentT.cs
index a6f821cc..599babb5 100644
--- a/Numerics/Distributions/Univariate/StudentT.cs
+++ b/Numerics/Distributions/Univariate/StudentT.cs
@@ -361,7 +361,7 @@ public ArgumentOutOfRangeException ValidateParameters(double location, double sc
                     throw new ArgumentOutOfRangeException(nameof(DegreesOfFreedom), "The degrees of freedom ν (nu) must greater than or equal to one.");
                 return new ArgumentOutOfRangeException(nameof(DegreesOfFreedom), "The degrees of freedom ν (nu) must greater than or equal to one.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Triangular.cs b/Numerics/Distributions/Univariate/Triangular.cs
index 3a65a94f..e16e098b 100644
--- a/Numerics/Distributions/Univariate/Triangular.cs
+++ b/Numerics/Distributions/Univariate/Triangular.cs
@@ -334,7 +334,7 @@ private ArgumentOutOfRangeException ValidateParameters(double min, double mode,
                     throw new ArgumentOutOfRangeException(nameof(MostLikely), "The mode (most likely) must be between the min and max.");
                 return new ArgumentOutOfRangeException(nameof(MostLikely), "The mode (most likely) must be between the min and max.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/TruncatedDistribution.cs b/Numerics/Distributions/Univariate/TruncatedDistribution.cs
index 04275f56..6fb26359 100644
--- a/Numerics/Distributions/Univariate/TruncatedDistribution.cs
+++ b/Numerics/Distributions/Univariate/TruncatedDistribution.cs
@@ -273,7 +273,7 @@ public override void SetParameters(IList<double> parameters)
         /// <inheritdoc/>
         public override ArgumentOutOfRangeException ValidateParameters(IList<double> parameters, bool throwException)
         {
-            if (_baseDist != null) _baseDist.ValidateParameters(parameters.ToArray().Subset(0, parameters.Count - 2), throwException);
+            if (_baseDist != null!) _baseDist.ValidateParameters(parameters.ToArray().Subset(0, parameters.Count - 2), throwException);
             if (double.IsNaN(Min) || double.IsNaN(Max) || double.IsInfinity(Min) || double.IsInfinity(Max) || Min >= Max)
             {
                 if (throwException)
@@ -286,7 +286,7 @@ public override ArgumentOutOfRangeException ValidateParameters(IList<double> par
                     throw new ArgumentOutOfRangeException(nameof(Min), "Truncation interval has zero probability mass.");
                 return new ArgumentOutOfRangeException(nameof(Min), "Truncation interval has zero probability mass.");
             }              
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/TruncatedNormal.cs b/Numerics/Distributions/Univariate/TruncatedNormal.cs
index f1850f01..465352bd 100644
--- a/Numerics/Distributions/Univariate/TruncatedNormal.cs
+++ b/Numerics/Distributions/Univariate/TruncatedNormal.cs
@@ -386,7 +386,7 @@ public ArgumentOutOfRangeException ValidateParameters(double mean, double standa
                     throw new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than the max.");
                 return new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than the max.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Uncertainty Analysis/BootstrapAnalysis.cs b/Numerics/Distributions/Univariate/Uncertainty Analysis/BootstrapAnalysis.cs
index 93940d00..28c2b6b5 100644
--- a/Numerics/Distributions/Univariate/Uncertainty Analysis/BootstrapAnalysis.cs	
+++ b/Numerics/Distributions/Univariate/Uncertainty Analysis/BootstrapAnalysis.cs	
@@ -141,7 +141,7 @@ public IUnivariateDistribution[] Distributions()
 
                 // MLE and certain L-moments methods can fail to find a solution
                 // On fail, set to null
-                if (failed == true) bootDistributions[idx] = null;
+                if (failed == true) bootDistributions[idx] = null!;
 
             });
             return bootDistributions;
@@ -171,7 +171,7 @@ public IUnivariateDistribution[] Distributions(ParameterSet[] parameterSets)
                 };
 
                 // On fail, set to null
-                if (failed == true) bootDistributions[idx] = null;
+                if (failed == true) bootDistributions[idx] = null!;
 
             });
             return bootDistributions;
@@ -181,7 +181,7 @@ public IUnivariateDistribution[] Distributions(ParameterSet[] parameterSets)
         /// <summary>
         /// Bootstrap an array of distribution parameters.
         /// </summary>
-        public double[,] Parameters(IUnivariateDistribution[] distributions = null)
+        public double[,] Parameters(IUnivariateDistribution[] distributions = null!)
         {
             var bootDistributions = distributions != null ? distributions : Distributions();
             var bootParameters = new double[bootDistributions.Count(), Distribution.NumberOfParameters];
@@ -206,7 +206,7 @@ public IUnivariateDistribution[] Distributions(ParameterSet[] parameterSets)
         /// <summary>
         /// Bootstrap an array of distribution parameter sets.
         /// </summary>
-        public ParameterSet[] ParameterSets(IUnivariateDistribution[] distributions = null)
+        public ParameterSet[] ParameterSets(IUnivariateDistribution[] distributions = null!)
         {
             var bootDistributions = distributions != null ? distributions : Distributions();
             var bootParameters = new ParameterSet[bootDistributions.Count()];
@@ -293,7 +293,7 @@ public ParameterSet[] ParameterSets(IUnivariateDistribution[] distributions = nu
         /// <param name="alpha">The confidence level; Default = 0.1, which will result in the 90% confidence intervals.</param>
         /// <param name="distributions">Optional. Pass in an array of bootstrapped distributions. Default = null.</param>
         /// <param name="recordParameterSets">Optional. Determines whether to record parameter sets. Default = true.</param>
-        public UncertaintyAnalysisResults Estimate(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null, bool recordParameterSets = true)
+        public UncertaintyAnalysisResults Estimate(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null!, bool recordParameterSets = true)
         {
             var results = new UncertaintyAnalysisResults();
             results.ParentDistribution = (UnivariateDistributionBase)Distribution;
@@ -344,7 +344,7 @@ public UncertaintyAnalysisResults Estimate(IList<double> probabilities, double a
         /// <param name="quantiles">List quantile values.</param>
         /// <param name="probabilities">List of non-exceedance probabilities.</param>
         /// <param name="distributions">Optional. Pass in an array of bootstrapped distributions. Default = null.</param>
-        public double[] ExpectedProbabilities(IList<double> quantiles, IList<double> probabilities, IUnivariateDistribution[] distributions = null)
+        public double[] ExpectedProbabilities(IList<double> quantiles, IList<double> probabilities, IUnivariateDistribution[] distributions = null!)
         {
             var quants = quantiles.ToArray();
             var probs = probabilities.ToArray();
@@ -396,7 +396,7 @@ public double[] ExpectedProbabilities(IList<double> quantiles, IList<double> pro
         /// </summary>
         /// <param name="quantiles">List quantile values.</param>
         /// <param name="distributions">Optional. Pass in an array of bootstrapped distributions. Default = null.</param>
-        public double[] ExpectedProbabilities(IList<double> quantiles, IUnivariateDistribution[] distributions = null)
+        public double[] ExpectedProbabilities(IList<double> quantiles, IUnivariateDistribution[] distributions = null!)
         {
             var quants = quantiles.ToArray();
             Array.Sort(quants);
@@ -447,7 +447,7 @@ public double[] ComputeMinMaxQuantiles(double minProbability, double maxProbabil
         /// <param name="probabilities">List of non-exceedance probabilities.</param>
         /// <param name="alpha">The confidence level; Default = 0.1, which will result in the 90% confidence intervals.</param>
         /// <param name="distributions">Optional. Pass in an array of bootstrapped distributions. Default = null.</param>
-        public double[,] PercentileQuantileCI(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null)
+        public double[,] PercentileQuantileCI(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null!)
         {
             var CIs = new double[] { alpha / 2d, 1d - alpha / 2d };
             var Output = new double[probabilities.Count, 2];
@@ -474,7 +474,7 @@ public double[] ComputeMinMaxQuantiles(double minProbability, double maxProbabil
         /// <param name="probabilities">List of non-exceedance probabilities.</param>
         /// <param name="alpha">The confidence level; Default = 0.1, which will result in the 90% confidence intervals.</param>
         /// <param name="distributions">Optional. Pass in an array of bootstrapped distributions. Default = null.</param>
-        public double[,] BiasCorrectedQuantileCI(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null)
+        public double[,] BiasCorrectedQuantileCI(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null!)
         {
             // Create list of original X values given probability values
             var populationXValues = new double[probabilities.Count];
@@ -520,7 +520,7 @@ public double[] ComputeMinMaxQuantiles(double minProbability, double maxProbabil
         /// <param name="probabilities">List of non-exceedance probabilities.</param>
         /// <param name="alpha">The confidence level; Default = 0.1, which will result in the 90% confidence intervals.</param>
         /// <param name="distributions">Optional. Pass in an array of bootstrapped distributions. Default = null.</param>
-        public double[,] NormalQuantileCI(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null)
+        public double[,] NormalQuantileCI(IList<double> probabilities, double alpha = 0.1, IUnivariateDistribution[] distributions = null!)
         {
 
             // Create list of original X values given probability values
@@ -713,7 +713,7 @@ private double[] AccelerationConstants(IList<double> sampleData, IList<double> p
                 {
                     // MLE and certain L-moments methods can fail to find a solution
                     // On fail, set to null
-                    bootDistributions[i] = null;
+                    bootDistributions[i] = null!;
                     for (int j = 0; j < probabilities.Count; j++)
                     {
                         xValues[i, j] = double.NaN;
diff --git a/Numerics/Distributions/Univariate/Uncertainty Analysis/UncertaintyAnalysisResults.cs b/Numerics/Distributions/Univariate/Uncertainty Analysis/UncertaintyAnalysisResults.cs
index c463c299..29ff0a46 100644
--- a/Numerics/Distributions/Univariate/Uncertainty Analysis/UncertaintyAnalysisResults.cs	
+++ b/Numerics/Distributions/Univariate/Uncertainty Analysis/UncertaintyAnalysisResults.cs	
@@ -81,7 +81,7 @@ public UncertaintyAnalysisResults(UnivariateDistributionBase parentDistribution,
                                           double maxProbability = 1 - 1e-9,
                                           bool recordParameterSets = false)
         {
-            if (parentDistribution == null)
+            if (parentDistribution == null!)
                 throw new ArgumentNullException(nameof(parentDistribution));
             if (sampledDistributions == null || sampledDistributions.Length == 0)
                 throw new ArgumentException("Sampled distributions cannot be null or empty.", nameof(sampledDistributions));
@@ -106,27 +106,27 @@ public UncertaintyAnalysisResults(UnivariateDistributionBase parentDistribution,
         /// <summary>
         /// The parent probability distribution.
         /// </summary>
-        public UnivariateDistributionBase ParentDistribution { get; set; }
+        public UnivariateDistributionBase ParentDistribution { get; set; } = null!;
 
         /// <summary>
         /// The array of parameter sets.
         /// </summary>
-        public ParameterSet[] ParameterSets { get; set; }
+        public ParameterSet[] ParameterSets { get; set; } = null!;
 
         /// <summary>
         /// The confidence intervals. 
         /// </summary>
-        public double[,] ConfidenceIntervals { get; set; }
+        public double[,] ConfidenceIntervals { get; set; } = null!;
 
         /// <summary>
         /// The mode (or computed) curve from the parent distribution. 
         /// </summary>
-        public double[] ModeCurve { get; set; }
+        public double[] ModeCurve { get; set; } = null!;
 
         /// <summary>
         /// The mean (or predictive) curve. 
         /// </summary>
-        public double[] MeanCurve { get; set; }
+        public double[] MeanCurve { get; set; } = null!;
 
         /// <summary>
         /// Gets or sets the Akaike information criteria (AIC) of the fit.
@@ -189,7 +189,8 @@ public static UncertaintyAnalysisResults FromByteArray(byte[] bytes)
                 options.Converters.Add(new Double2DArrayConverter());
                 options.Converters.Add(new String2DArrayConverter());
                 options.Converters.Add(new UnivariateDistributionConverter());
-                return JsonSerializer.Deserialize<UncertaintyAnalysisResults>(bytes, options);
+                var result = JsonSerializer.Deserialize<UncertaintyAnalysisResults>(bytes, options);
+                return result ?? FromByteArrayLegacy(bytes);
             }
             catch (Exception)
             {
@@ -226,7 +227,7 @@ private static UncertaintyAnalysisResults FromByteArrayLegacy(byte[] bytes)
                 // If there is an error, just catch it and force the user to rerun the
                 // uncertainty analysis. 
             }
-            return null;
+            return null!;
         }
 
         /// <summary>
@@ -235,7 +236,7 @@ private static UncertaintyAnalysisResults FromByteArrayLegacy(byte[] bytes)
         public XElement ToXElement()
         {
             var result = new XElement(nameof(UncertaintyAnalysisResults));         
-            if (ParentDistribution != null) result.Add(ParentDistribution.ToXElement());
+            if (ParentDistribution != null!) result.Add(ParentDistribution.ToXElement());
             result.SetAttributeValue(nameof(AIC), AIC.ToString("G17", CultureInfo.InvariantCulture));
             result.SetAttributeValue(nameof(BIC), BIC.ToString("G17", CultureInfo.InvariantCulture));
             result.SetAttributeValue(nameof(DIC), DIC.ToString("G17", CultureInfo.InvariantCulture));
@@ -268,44 +269,62 @@ public static UncertaintyAnalysisResults FromXElement(XElement xElement)
         {
             var ua = new UncertaintyAnalysisResults();    
             // Parent distribution
-            if (xElement.Element("Distribution") != null)
-                ua.ParentDistribution = UnivariateDistributionFactory.CreateDistribution(xElement.Element("Distribution"));
+            var distElement = xElement.Element("Distribution");
+            if (distElement != null)
+            {
+                var parentDist = UnivariateDistributionFactory.CreateDistribution(distElement);
+                if (parentDist is not null)
+                {
+                    ua.ParentDistribution = parentDist;
+                }
+                else
+                {
+                    throw new InvalidDataException("Unable to deserialize parent distribution from XElement.");
+                }    
+            }
+                
 
             // AIC
-            if (xElement.Attribute(nameof(AIC)) != null)
+            var aicElement = xElement.Attribute(nameof(AIC));
+            if (aicElement != null)
             {
-                double.TryParse(xElement.Attribute(nameof(AIC)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var aic);
+                double.TryParse(aicElement.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var aic);
                 ua.AIC = aic;
             }
             // BIC
-            if (xElement.Attribute(nameof(BIC)) != null)
+            var bicElement = xElement.Attribute(nameof(BIC));
+            if (bicElement != null)
             {
-                double.TryParse(xElement.Attribute(nameof(BIC)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var bic);
+                double.TryParse(bicElement.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var bic);
                 ua.BIC = bic;
             }
             // DIC
-            if (xElement.Attribute(nameof(DIC)) != null)
+            var dicElement = xElement.Attribute(nameof(DIC));
+            if (dicElement != null)
             {
-                double.TryParse(xElement.Attribute(nameof(DIC)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var dic);
+                double.TryParse(dicElement.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var dic);
                 ua.DIC = dic;
             }
             // RMSE
-            if (xElement.Attribute(nameof(RMSE)) != null)
+            var rmseElement = xElement.Attribute(nameof(RMSE));
+            if (rmseElement != null)
             {
-                double.TryParse(xElement.Attribute(nameof(RMSE)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var rmse);
+                double.TryParse(rmseElement.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var rmse);
                 ua.RMSE = rmse;
             }
             // ERL
-            if (xElement.Attribute(nameof(ERL)) != null)
+            var erlElement = xElement.Attribute(nameof(ERL));
+            if (erlElement != null)
             {
-                double.TryParse(xElement.Attribute(nameof(ERL)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var erl);
+                double.TryParse(erlElement.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var erl);
                 ua.ERL = erl;
             }
 
             // Mode Curve
-            if (xElement.Attribute(nameof(ua.ModeCurve)) != null)
+            var modeAttr = xElement.Attribute(nameof(ua.ModeCurve));
+            if (modeAttr != null)
             {
-                var vals = xElement.Attribute(nameof(ua.ModeCurve)).Value.Split('|');
+                var vals = modeAttr.Value.Split('|');
                 if (vals.Length > 0)
                 {
                     ua.ModeCurve = new double[vals.Length];
@@ -316,9 +335,10 @@ public static UncertaintyAnalysisResults FromXElement(XElement xElement)
                 }
             }
             // Mean Curve
-            if (xElement.Attribute(nameof(ua.MeanCurve)) != null)
+            var meanAttr = xElement.Attribute(nameof(ua.MeanCurve));
+            if (meanAttr != null)
             {
-                var vals = xElement.Attribute(nameof(ua.MeanCurve)).Value.Split('|');
+                var vals = meanAttr.Value.Split('|');
                 if (vals.Length > 0)
                 {
                     ua.MeanCurve = new double[vals.Length];
@@ -329,9 +349,10 @@ public static UncertaintyAnalysisResults FromXElement(XElement xElement)
                 }
             }
             // Confidence Intervals
-            if (xElement.Attribute(nameof(ua.ConfidenceIntervals)) != null)
+            var ciAttr = xElement.Attribute(nameof(ua.ConfidenceIntervals));
+            if (ciAttr != null)
             {
-                var vals = xElement.Attribute(nameof(ua.ConfidenceIntervals)).Value.Split('|');
+                var vals = ciAttr.Value.Split('|');
                 if (vals.Length > 0)
                 {
                     ua.ConfidenceIntervals = new double[vals.Length, vals[0].Split(',').Length];
@@ -355,7 +376,7 @@ public static UncertaintyAnalysisResults FromXElement(XElement xElement)
         /// <param name="probabilities">Array of non-exceedance probabilities.</param>
         public void ProcessModeCurve(UnivariateDistributionBase parentDistribution, double[] probabilities)
         {
-            if (parentDistribution == null)
+            if (parentDistribution == null!)
                 throw new ArgumentNullException(nameof(parentDistribution));
             if (probabilities == null || probabilities.Length == 0)
                 throw new ArgumentException("Probabilities cannot be null or empty.", nameof(probabilities));
@@ -442,7 +463,7 @@ public void ProcessMeanCurve(UnivariateDistributionBase[] sampledDistributions,
 
             Parallel.For(0, B, j =>
             {
-                if (sampledDistributions[j] != null)
+                if (sampledDistributions[j] != null!)
                 {
                     var innerMin = sampledDistributions[j].InverseCDF(minProbability);
                     var innerMax = sampledDistributions[j].InverseCDF(maxProbability);
@@ -478,7 +499,7 @@ public void ProcessMeanCurve(UnivariateDistributionBase[] sampledDistributions,
                 double total = 0d;
                 Parallel.For(0, B, () => 0d, (j, loop, sum) =>
                 {
-                    if (sampledDistributions[j] != null)
+                    if (sampledDistributions[j] != null!)
                     {
                         sum += sampledDistributions[j].CDF(quantiles[i]);
                     }
@@ -530,7 +551,7 @@ public void ProcessParameterSets(UnivariateDistributionBase[] sampledDistributio
 
             Parallel.For(0, B, idx =>
             {
-                if (sampledDistributions[idx] != null)
+                if (sampledDistributions[idx] != null!)
                 {
                     ParameterSets[idx] = new ParameterSet(sampledDistributions[idx].GetParameters, double.NaN);
                 }
diff --git a/Numerics/Distributions/Univariate/Uniform.cs b/Numerics/Distributions/Univariate/Uniform.cs
index cea983c7..9497e43f 100644
--- a/Numerics/Distributions/Univariate/Uniform.cs
+++ b/Numerics/Distributions/Univariate/Uniform.cs
@@ -251,7 +251,7 @@ public ArgumentOutOfRangeException ValidateParameters(double min, double max, bo
                     throw new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than the max.");
                 return new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than the max.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/UniformDiscrete.cs b/Numerics/Distributions/Univariate/UniformDiscrete.cs
index f5f6500d..ab603bec 100644
--- a/Numerics/Distributions/Univariate/UniformDiscrete.cs
+++ b/Numerics/Distributions/Univariate/UniformDiscrete.cs
@@ -259,7 +259,7 @@ public ArgumentOutOfRangeException ValidateParameters(double min, double max, bo
                     throw new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than the max.");
                 return new ArgumentOutOfRangeException(nameof(Min), "The min cannot be greater than the max.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Distributions/Univariate/Weibull.cs b/Numerics/Distributions/Univariate/Weibull.cs
index f3683016..1f8a5bc4 100644
--- a/Numerics/Distributions/Univariate/Weibull.cs
+++ b/Numerics/Distributions/Univariate/Weibull.cs
@@ -304,7 +304,7 @@ public ArgumentOutOfRangeException ValidateParameters(double scale, double shape
                     throw new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be positive.");
                 return new ArgumentOutOfRangeException(nameof(Kappa), "The shape parameter κ (kappa) must be positive.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Functions/LinearFunction.cs b/Numerics/Functions/LinearFunction.cs
index 2902b269..9d4dee0f 100644
--- a/Numerics/Functions/LinearFunction.cs
+++ b/Numerics/Functions/LinearFunction.cs
@@ -173,7 +173,7 @@ public ArgumentOutOfRangeException ValidateParameters(IList<double> parameters,
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "Standard error must be greater than zero.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "Standard error must be greater than zero.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Functions/PowerFunction.cs b/Numerics/Functions/PowerFunction.cs
index 08250343..900ac1e7 100644
--- a/Numerics/Functions/PowerFunction.cs
+++ b/Numerics/Functions/PowerFunction.cs
@@ -200,7 +200,7 @@ public ArgumentOutOfRangeException ValidateParameters(IList<double> parameters,
                     throw new ArgumentOutOfRangeException(nameof(Sigma), "Standard error must be greater than zero.");
                 return new ArgumentOutOfRangeException(nameof(Sigma), "Standard error must be greater than zero.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Functions/TabularFunction.cs b/Numerics/Functions/TabularFunction.cs
index ab741110..8e374875 100644
--- a/Numerics/Functions/TabularFunction.cs
+++ b/Numerics/Functions/TabularFunction.cs
@@ -159,7 +159,7 @@ public ArgumentOutOfRangeException ValidateParameters(IList<double> parameters,
                     throw new ArgumentOutOfRangeException(nameof(PairedData), "The uncertain ordered paired data has errors.");
                 return new ArgumentOutOfRangeException(nameof(PairedData), "The uncertain ordered paired data has errors.");
             }
-            return null;
+            return null!;
         }
 
         /// <inheritdoc/>
diff --git a/Numerics/Machine Learning/Supervised/DecisionTree.cs b/Numerics/Machine Learning/Supervised/DecisionTree.cs
index e3a9d35f..bab82c7d 100644
--- a/Numerics/Machine Learning/Supervised/DecisionTree.cs	
+++ b/Numerics/Machine Learning/Supervised/DecisionTree.cs	
@@ -456,7 +456,7 @@ public double[] Predict(double[,] X)
         /// <param name="X">The matrix of predictors.</param>
         public double[] Predict(Matrix X)
         {
-            if (!IsTrained || X.NumberOfColumns != Dimensions) return null;
+            if (!IsTrained || X.NumberOfColumns != Dimensions) return null!;
             var result = new double[X.NumberOfRows];
             for (int i = 0; i < X.NumberOfRows; i++)
             {
diff --git a/Numerics/Machine Learning/Supervised/GeneralizedLinearModel.cs b/Numerics/Machine Learning/Supervised/GeneralizedLinearModel.cs
index 19aed4db..e36ff85c 100644
--- a/Numerics/Machine Learning/Supervised/GeneralizedLinearModel.cs	
+++ b/Numerics/Machine Learning/Supervised/GeneralizedLinearModel.cs	
@@ -118,7 +118,7 @@ public GeneralizedLinearModel(Matrix x, Vector y, bool hasIntercept = true, Link
         /// <summary>
         /// The list of estimated parameter values.
         /// </summary>
-        public double[] Parameters { get; private set; }
+        public double[] Parameters { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// The list of the estimated parameter names. 
@@ -128,27 +128,27 @@ public GeneralizedLinearModel(Matrix x, Vector y, bool hasIntercept = true, Link
         /// <summary>
         /// The list of the estimated parameter standard errors. 
         /// </summary>
-        public double[] ParameterStandardErrors { get; private set; }
+        public double[] ParameterStandardErrors { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// The list of the estimated parameter z-scores.
         /// </summary>
-        public double[] ParameterZScores { get; private set; }
+        public double[] ParameterZScores { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// The list of the estimated parameter p-values.
         /// </summary>
-        public double[] ParameterPValues { get; private set; }
+        public double[] ParameterPValues { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// The estimate parameter covariance matrix. 
         /// </summary>
-        public Matrix Covariance { get; private set; }
+        public Matrix Covariance { get; private set; } = new Matrix(1, 1);
 
         /// <summary>
         /// The residuals of the fitted linear model. 
         /// </summary>
-        public double[] Residuals { get; private set; }
+        public double[] Residuals { get; private set; } = Array.Empty<double>();
 
         /// <summary>
         /// The model standard error.
@@ -188,7 +188,7 @@ public GeneralizedLinearModel(Matrix x, Vector y, bool hasIntercept = true, Link
         /// <summary>
         /// Gets the optimizer used to train the model. Default = Nelder-Mead.
         /// </summary>
-        public Optimizer Optimizer { get; private set; }
+        public Optimizer Optimizer { get; private set; } = null!;
 
         /// <summary>
         /// Gets the link function type. 
diff --git a/Numerics/Machine Learning/Supervised/KNearestNeighbors.cs b/Numerics/Machine Learning/Supervised/KNearestNeighbors.cs
index 24b4e42f..f8c7bdfd 100644
--- a/Numerics/Machine Learning/Supervised/KNearestNeighbors.cs	
+++ b/Numerics/Machine Learning/Supervised/KNearestNeighbors.cs	
@@ -271,7 +271,7 @@ public double[] BootstrapPredict(Matrix X, int seed = -1)
         /// <param name="xTest">The test matrix of predictors</param>
         private int[] kNN(Matrix xTrain, Vector yTrain, Matrix xTest)
         {
-            if (NumberOfFeatures != xTrain.NumberOfColumns) return null;
+            if (NumberOfFeatures != xTrain.NumberOfColumns) return null!;
             int R = xTest.NumberOfRows;
             var result = new int[K];
             for (int i = 0; i < R; i++)
@@ -306,7 +306,7 @@ private int[] kNN(Matrix xTrain, Vector yTrain, Matrix xTest)
         /// <param name="xTest">The test matrix of predictors</param>
         private double[] kNNPredict(Matrix xTrain, Vector yTrain, Matrix xTest)
         {
-            if (xTest.NumberOfColumns != xTrain.NumberOfColumns) return null;
+            if (xTest.NumberOfColumns != xTrain.NumberOfColumns) return null!;
             int R = xTest.NumberOfRows;
             var result = new double[R];
             for (int i = 0; i < R; i++)
diff --git a/Numerics/Machine Learning/Supervised/NaiveBayes.cs b/Numerics/Machine Learning/Supervised/NaiveBayes.cs
index 3a16ef9d..7a47721b 100644
--- a/Numerics/Machine Learning/Supervised/NaiveBayes.cs	
+++ b/Numerics/Machine Learning/Supervised/NaiveBayes.cs	
@@ -143,17 +143,17 @@ public NaiveBayes(Matrix x, Vector y)
         /// <summary>
         /// The means of each feature given each class. 
         /// </summary>
-        public double[,] Means { get; private set; }
+        public double[,] Means { get; private set; } = null!;
 
         /// <summary>
         /// The standard deviations of each feature given each class.
         /// </summary>
-        public double[,] StandardDeviations { get; private set; }
+        public double[,] StandardDeviations { get; private set; } = null!;
 
         /// <summary>
         /// The prior probability for each class.
         /// </summary>
-        public double[] Priors { get; private set; }
+        public double[] Priors { get; private set; } = null!;
 
         /// <summary>
         /// Determines if the classifier has been trained.
@@ -245,7 +245,7 @@ public double[] Predict(double[,] X)
         /// <param name="X">The matrix of predictors.</param>
         public double[] Predict(Matrix X)
         {
-            if (!IsTrained || X.NumberOfColumns != this.X.NumberOfColumns) return null;
+            if (!IsTrained || X.NumberOfColumns != this.X.NumberOfColumns) return null!;
             var result = new double[X.NumberOfRows];
             for (int i = 0; i < X.NumberOfRows; i++)
             {
diff --git a/Numerics/Machine Learning/Supervised/RandomForest.cs b/Numerics/Machine Learning/Supervised/RandomForest.cs
index 5d8e6b13..94da4efe 100644
--- a/Numerics/Machine Learning/Supervised/RandomForest.cs	
+++ b/Numerics/Machine Learning/Supervised/RandomForest.cs	
@@ -166,7 +166,7 @@ public RandomForest(Matrix x, Vector y, int seed = -1)
         /// <summary>
         /// The array of decision trees.
         /// </summary>
-        public DecisionTree[] DecisionTrees { get; private set; }
+        public DecisionTree[] DecisionTrees { get; private set; } = null!;
 
         /// <summary>
         /// Determines whether this is for regression or classification. Default = regression.
@@ -250,7 +250,7 @@ private DecisionTree BootstrapDecisionTree(int seed = -1)
         /// <param name="alpha">The confidence level; Default = 0.1, which will result in the 90% confidence intervals.</param>
         public double[,] Predict(Matrix X, double alpha = 0.1)
         {
-            if (!IsTrained) return null;
+            if (!IsTrained) return null!;
 
             var percentiles = new double[] { alpha / 2d, 0.5, 1d - alpha / 2d };
             var output = new double[X.NumberOfRows, 4]; // lower, median, upper, mean
diff --git a/Numerics/Machine Learning/Support/DecisionNode.cs b/Numerics/Machine Learning/Support/DecisionNode.cs
index 292e3c43..4a7d0014 100644
--- a/Numerics/Machine Learning/Support/DecisionNode.cs	
+++ b/Numerics/Machine Learning/Support/DecisionNode.cs	
@@ -54,12 +54,12 @@ public class DecisionNode
         /// <summary>
         /// Nodes to the left of the threshold.
         /// </summary>
-        public DecisionNode Left { get; set; } = null;
+        public DecisionNode Left { get; set; } = null!;
 
         /// <summary>
         /// Nodes to the right of the threshold.
         /// </summary>
-        public DecisionNode Right { get; set; } = null;
+        public DecisionNode Right { get; set; } = null!;
 
         /// <summary>
         /// The leaf node value.
diff --git a/Numerics/Machine Learning/Unsupervised/GaussianMixtureModel.cs b/Numerics/Machine Learning/Unsupervised/GaussianMixtureModel.cs
index 3429cd32..25ccdeb1 100644
--- a/Numerics/Machine Learning/Unsupervised/GaussianMixtureModel.cs	
+++ b/Numerics/Machine Learning/Unsupervised/GaussianMixtureModel.cs	
@@ -152,12 +152,12 @@ public GaussianMixtureModel(Matrix X, int k)
         /// <summary>
         /// The mixing weights 
         /// </summary>
-        public double[] Weights { get; private set; }
+        public double[] Weights { get; private set; } = null!;
 
         /// <summary>
         /// The likelihood of each data point (row) and for each cluster (column).
         /// </summary>
-        public double[,] LikelihoodMatrix { get; private set; }
+        public double[,] LikelihoodMatrix { get; private set; } = null!;
 
         /// <summary>
         /// The total log-likelihood of the fit.
diff --git a/Numerics/Machine Learning/Unsupervised/JenksNaturalBreaks.cs b/Numerics/Machine Learning/Unsupervised/JenksNaturalBreaks.cs
index 81ab280f..c2df6e79 100644
--- a/Numerics/Machine Learning/Unsupervised/JenksNaturalBreaks.cs	
+++ b/Numerics/Machine Learning/Unsupervised/JenksNaturalBreaks.cs	
@@ -128,12 +128,12 @@ public JenksNaturalBreaks(IList<float> data, int numberOfClusters, bool isDataSo
         /// <summary>
         /// Gets the array of estimated clusters.
         /// </summary>
-        public JenksCluster[] Clusters { get; private set; }
+        public JenksCluster[] Clusters { get; private set; } = null!;
 
         /// <summary>
         /// The array of break points.
         /// </summary>
-        public double[] Breaks { get; private set; }
+        public double[] Breaks { get; private set; } = null!;
 
         /// <summary>
         /// The goodness of fit measure. The closer to 1, the better the fit.
diff --git a/Numerics/Mathematics/Differentiation/NumericalDerivative.cs b/Numerics/Mathematics/Differentiation/NumericalDerivative.cs
index 848cca9e..312c2fa6 100644
--- a/Numerics/Mathematics/Differentiation/NumericalDerivative.cs
+++ b/Numerics/Mathematics/Differentiation/NumericalDerivative.cs
@@ -427,8 +427,8 @@ public static double SecondDerivativeBackward(Func<double, double> f, double poi
         public static double[,] Jacobian(
             Func<double[], double[]> g,
             double[] theta,
-            double[]? lowerBounds = null,
-            double[]? upperBounds = null,
+            double[] lowerBounds = null!,
+            double[] upperBounds = null!,
             double relStep = 1e-5,
             double absStep = 1e-7,
             int maxBacktrack = 5)
@@ -619,8 +619,8 @@ public static double SecondDerivativeBackward(Func<double, double> f, double poi
         public static double[] Gradient(
             Func<double[], double> f,
             double[] theta,
-            double[]? lowerBounds = null,
-            double[]? upperBounds = null,
+            double[] lowerBounds = null!,
+            double[] upperBounds = null!,
             double relStep = 1e-5,
             double absStep = 1e-7,
             int maxBacktrack = 5)
@@ -798,8 +798,8 @@ public static double[] Gradient(
         public static double[,] Hessian(
             Func<double[], double> f,
             double[] theta,
-            double[]? lowerBounds = null,
-            double[]? upperBounds = null,
+            double[] lowerBounds = null!,
+            double[] upperBounds = null!,
             double relStep = 1e-4,
             double absStep = 1e-6,
             int maxBacktrack = 6)
diff --git a/Numerics/Mathematics/Fourier Methods/Fourier.cs b/Numerics/Mathematics/Fourier Methods/Fourier.cs
index 21d50ec3..922a56ce 100644
--- a/Numerics/Mathematics/Fourier Methods/Fourier.cs	
+++ b/Numerics/Mathematics/Fourier Methods/Fourier.cs	
@@ -283,7 +283,7 @@ public static double[] Correlation(double[] data1, double[] data2)
             if (lagMax < 0)
                 lagMax = (int)Math.Floor(Math.Min(10d * Math.Log10(n), n - 1));
             if (lagMax < 1 || n < 2)
-                return null;
+                return null!;
             // Pad the length to be the power of 2 to facilitate FFT speed.
             int newLength = Convert.ToInt32(Math.Pow(2d, Math.Ceiling(Math.Log(series.Count, 2d))));
             // Normalize the data series
diff --git a/Numerics/Mathematics/Integration/AdaptiveGuassKronrod.cs b/Numerics/Mathematics/Integration/AdaptiveGuassKronrod.cs
index ec1fb7e5..f942fd0b 100644
--- a/Numerics/Mathematics/Integration/AdaptiveGuassKronrod.cs
+++ b/Numerics/Mathematics/Integration/AdaptiveGuassKronrod.cs
@@ -186,10 +186,10 @@ public override void Integrate()
                     Status = IntegrationStatus.Success;
                 }
             }
-            catch (Exception ex)
+            catch (Exception)
             {
                 Status = IntegrationStatus.Failure;
-                if (ReportFailure) throw ex;
+                if (ReportFailure) throw;
             }
         }
 
@@ -231,10 +231,10 @@ public void Integrate(List<StratificationBin> bins)
                     Status = IntegrationStatus.Success;
                 }
             }
-            catch (Exception ex)
+            catch (Exception)
             {
                 Status = IntegrationStatus.Failure;
-                if (ReportFailure) throw ex;
+                if (ReportFailure) throw;
             }
         }
 
diff --git a/Numerics/Mathematics/Integration/AdaptiveSimpsonsRule2D.cs b/Numerics/Mathematics/Integration/AdaptiveSimpsonsRule2D.cs
index 81dbda07..5539218e 100644
--- a/Numerics/Mathematics/Integration/AdaptiveSimpsonsRule2D.cs
+++ b/Numerics/Mathematics/Integration/AdaptiveSimpsonsRule2D.cs
@@ -170,10 +170,10 @@ public override void Integrate()
                     Status = IntegrationStatus.Success;
                 }
             }
-            catch (Exception ex)
+            catch (Exception)
             {
                 Status = IntegrationStatus.Failure;
-                if (ReportFailure) throw ex;
+                if (ReportFailure) throw;
             }
         }
 
diff --git a/Numerics/Mathematics/Integration/Miser.cs b/Numerics/Mathematics/Integration/Miser.cs
index 15dde370..215feddf 100644
--- a/Numerics/Mathematics/Integration/Miser.cs
+++ b/Numerics/Mathematics/Integration/Miser.cs
@@ -340,7 +340,7 @@ private void miser(Func<double[], double> function, double[] regn, int npts, dou
         private void ranpt(double[] pt, double[] regn)
         {
             int j, n = pt.Length;
-            double[] rnd = null;
+            double[] rnd = null!;
             if (UseSobolSequence)
                 rnd = _sobol.NextDouble();
 
diff --git a/Numerics/Mathematics/Integration/Support/Integrator.cs b/Numerics/Mathematics/Integration/Support/Integrator.cs
index f9aac968..e9baeceb 100644
--- a/Numerics/Mathematics/Integration/Support/Integrator.cs
+++ b/Numerics/Mathematics/Integration/Support/Integrator.cs
@@ -150,7 +150,7 @@ protected virtual void Validate()
         /// </summary>
         /// <param name="status">Optimization status.</param>
         /// <param name="exception">Inner exception.</param>
-        protected virtual void UpdateStatus(IntegrationStatus status, Exception? exception = null)
+        protected virtual void UpdateStatus(IntegrationStatus status, Exception exception = null!)
         {
             Status = status;
             if (status == IntegrationStatus.Failure)
diff --git a/Numerics/Mathematics/Integration/Vegas.cs b/Numerics/Mathematics/Integration/Vegas.cs
index fc3a93bb..39205954 100644
--- a/Numerics/Mathematics/Integration/Vegas.cs
+++ b/Numerics/Mathematics/Integration/Vegas.cs
@@ -112,7 +112,7 @@ public Vegas(Func<double[], double, double> function, int dimensions, IList<doub
 
         }
 
-        private double[] _region;
+        private double[] _region = null!;
         private int _numberOfBins = 50;
         private double _standardError;
         private double _chiSquared;
@@ -129,16 +129,16 @@ public Vegas(Func<double[], double, double> function, int dimensions, IList<doub
         private double totalErrorEstimate, weight, jacobianDeterminant, randomBinIndex, normalizedBinCount, binSpacingDelta, sumChiSquared, sumWeightedResults, sumIntegrationWeights;
 
 
-        private int[] binIndexes;
-        private int[] regionBinCounts;
-        private double[] dimensionContributions;
-        private double[] dx;
-        private double[] binDensities;
-        private double[] randomPoint;
-        private double[] binEdges;
-        private double[,] binContributions;
-        private double[,] refinedBinContributions;
-        private double[,] stratificationGrid;
+        private int[] binIndexes = null!;
+        private int[] regionBinCounts = null!;
+        private double[] dimensionContributions = null!;
+        private double[] dx = null!;
+        private double[] binDensities = null!;
+        private double[] randomPoint = null!;
+        private double[] binEdges = null!;
+        private double[,] binContributions = null!;
+        private double[,] refinedBinContributions = null!;
+        private double[,] stratificationGrid = null!;
 
 
         /// <summary>
diff --git a/Numerics/Mathematics/Linear Algebra/Support/Matrix.cs b/Numerics/Mathematics/Linear Algebra/Support/Matrix.cs
index a2f5c333..3688d63a 100644
--- a/Numerics/Mathematics/Linear Algebra/Support/Matrix.cs	
+++ b/Numerics/Mathematics/Linear Algebra/Support/Matrix.cs	
@@ -186,8 +186,10 @@ public Matrix(XElement xElement)
                 
           
             int nrow = 0, ncol = 0;
-            if (xElement.Attribute(nameof(NumberOfRows)) != null) int.TryParse(xElement.Attribute(nameof(NumberOfRows)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out nrow);
-            if (xElement.Attribute(nameof(NumberOfColumns)) != null) int.TryParse(xElement.Attribute(nameof(NumberOfColumns)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out ncol);
+            var rowsAttr = xElement.Attribute(nameof(NumberOfRows));
+            var colsAttr = xElement.Attribute(nameof(NumberOfColumns));
+            if (rowsAttr != null) int.TryParse(rowsAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out nrow);
+            if (colsAttr != null) int.TryParse(colsAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out ncol);
 
             _matrix = new double[nrow, ncol];
             int rowCount = 0;
@@ -219,7 +221,7 @@ public Matrix(XElement xElement)
 
         #region Members
 
-        private double[,] _matrix;
+        private double[,] _matrix = null!;
 
         /// <summary>
         /// Gets the number of rows.
@@ -251,7 +253,7 @@ public int NumberOfColumns
         /// <summary>
         /// The matrix column header text. 
         /// </summary>
-        public string[] Header { get; set; }
+        public string[] Header { get; set; } = null!;
 
         /// <summary>
         /// Evaluates whether this matrix is symmetric.
diff --git a/Numerics/Mathematics/Linear Algebra/Support/Vector.cs b/Numerics/Mathematics/Linear Algebra/Support/Vector.cs
index eb687253..b72e1416 100644
--- a/Numerics/Mathematics/Linear Algebra/Support/Vector.cs	
+++ b/Numerics/Mathematics/Linear Algebra/Support/Vector.cs	
@@ -108,10 +108,10 @@ public double this[int index]
             set { _vector[index] = value; }
         }
 
-         /// <summary>
-         /// The vector header text. 
-         /// </summary>
-        public string Header { get; set; }
+        /// <summary>
+        /// The vector header text. 
+        /// </summary>
+        public string Header { get; set; } = null!;
 
 
         /// <summary>
diff --git a/Numerics/Mathematics/Optimization/Dynamic/Dijkstra.cs b/Numerics/Mathematics/Optimization/Dynamic/Dijkstra.cs
index 1f0bcdf7..05197518 100644
--- a/Numerics/Mathematics/Optimization/Dynamic/Dijkstra.cs
+++ b/Numerics/Mathematics/Optimization/Dynamic/Dijkstra.cs
@@ -94,7 +94,7 @@ public static bool PathExists(float[,] resultTable, int nodeIndex)
         /// <param name="nodeCount">Optional number of nodes in the network. If not provided it will be calculated internally.</param>
         /// <param name="edgesFromNodes">Optional list of incoming edges from each node in the network. If not provided or mismatched with edges it will be calculated internally.</param>
         /// <returns>Lookup table of shortest paths from any given node.</returns>
-        public static float[,] Solve(IList<Edge> edges, int[] destinationIndices, int nodeCount = -1, List<Edge>[] edgesFromNodes = null)
+        public static float[,] Solve(IList<Edge> edges, int[] destinationIndices, int nodeCount = -1, List<Edge>[] edgesFromNodes = null!)
         {
             // Set optional parameters if required.
             int nNodes = (nodeCount == -1) ? (edges.Max(o => Math.Max(o.FromIndex,o.ToIndex)) + 1) : nodeCount;
@@ -147,7 +147,7 @@ public static bool PathExists(float[,] resultTable, int nodeIndex)
         /// <param name="nodeCount">Optional number of nodes in the network. If not provided it will be calculated internally.</param>
         /// <param name="edgesToNodes">Optional list of incoming edges from each node in the network. If not provided or mismatched with edges it will be calculated internally.</param>
         /// <returns>Lookup table of shortest paths from any given node.</returns>
-        public static float[,] Solve(IList<Edge> edges, int destinationIndex, int nodeCount = -1, List<Edge>[] edgesToNodes = null)
+        public static float[,] Solve(IList<Edge> edges, int destinationIndex, int nodeCount = -1, List<Edge>[] edgesToNodes = null!)
         {
             // Set optional parameters if required.
             int nNodes = (nodeCount == -1) ? (edges.Max(o => Math.Max(o.FromIndex, o.ToIndex)) + 1) : nodeCount;
diff --git a/Numerics/Mathematics/Optimization/Dynamic/Network.cs b/Numerics/Mathematics/Optimization/Dynamic/Network.cs
index ac5bdb50..6c4968b6 100644
--- a/Numerics/Mathematics/Optimization/Dynamic/Network.cs
+++ b/Numerics/Mathematics/Optimization/Dynamic/Network.cs
@@ -166,7 +166,7 @@ public List<int> GetPath(int[] edgesToRemove, int startNodeIndex)
                 }
             }
             // if n = 0, then no roads to escape to
-            if (heap.Count == 0) return null;
+            if (heap.Count == 0) return null!;
 
             float tempWeight;
             int tempIndex;
@@ -345,7 +345,7 @@ public List<int> GetPath(int[] edgesToRemove, int startNodeIndex)
 
                 return UpdatedPath;
             }
-            else return null;
+            else return null!;
         }
 
         public List<int> GetPath(int[] edgesToRemove, int startNodeIndex, float[,] existingResultsTable)
@@ -407,7 +407,7 @@ public List<int> GetPath(int[] edgesToRemove, int startNodeIndex, float[,] exist
             }
 
             //if n = 0 then no roads to escape to
-            if (heap.Count == 0) return null;
+            if (heap.Count == 0) return null!;
 
             float tempWeight;
             int tempIndex;
diff --git a/Numerics/Mathematics/Optimization/Global/MLSL.cs b/Numerics/Mathematics/Optimization/Global/MLSL.cs
index 4525f536..01c066bf 100644
--- a/Numerics/Mathematics/Optimization/Global/MLSL.cs
+++ b/Numerics/Mathematics/Optimization/Global/MLSL.cs
@@ -175,12 +175,12 @@ public MLSL(Func<double[], double> objectiveFunction, int numberOfParameters, IL
         /// <summary>
         /// The list of all sampled points.
         /// </summary>
-        public List<SamplePoint> SampledPoints { get; private set; }
+        public List<SamplePoint> SampledPoints { get; private set; } = null!;
 
         /// <summary>
         /// The list of all local optimums.
         /// </summary>
-        public List<SamplePoint> LocalMinimums { get; private set; }
+        public List<SamplePoint> LocalMinimums { get; private set; } = null!;
 
         /// <summary>
         /// The minimum number of iterations to carry out with no improvement. Default = 5.
@@ -207,7 +207,7 @@ protected override void Optimize()
             double oldFit = double.MaxValue;
             int noImprovement = 0;
             bool cancel = false;
-            Optimizer solver = null;
+            Optimizer solver = null!;
             var prng = new MersenneTwister(PRNGSeed);
 
             // Set lower and upper bounds and
@@ -378,7 +378,7 @@ protected override void Optimize()
         private Optimizer GetLocalOptimizer(IList<double> initialValues, double relativeTolerance, double absoluteTolerance, ref bool cancel)
         {
             bool localCancel = false;
-            Optimizer solver = null;
+            Optimizer solver = null!;
 
             // Make sure the parameters are within the bounds.
             for (int i = 0; i < NumberOfParameters; i++)  
diff --git a/Numerics/Mathematics/Optimization/Global/MultiStart.cs b/Numerics/Mathematics/Optimization/Global/MultiStart.cs
index 56b5d71b..05ac9006 100644
--- a/Numerics/Mathematics/Optimization/Global/MultiStart.cs
+++ b/Numerics/Mathematics/Optimization/Global/MultiStart.cs
@@ -163,7 +163,7 @@ protected override void Optimize()
         {
             int i, j, D = NumberOfParameters;
             bool cancel = false;
-            Optimizer solver = null;
+            Optimizer solver = null!;
 
             // Set lower and upper bounds and
             // create uniform distributions for each parameter
@@ -219,7 +219,7 @@ protected override void Optimize()
         private Optimizer GetLocalOptimizer(IList<double> initialValues, double relativeTolerance, double absoluteTolerance, ref bool cancel)
         {
             bool localCancel = false;
-            Optimizer solver = null;
+            Optimizer solver = null!;
 
             // Make sure the parameters are within the bounds.
             for (int i = 0; i < NumberOfParameters; i++)
diff --git a/Numerics/Mathematics/Optimization/Global/ParticleSwarm.cs b/Numerics/Mathematics/Optimization/Global/ParticleSwarm.cs
index 24d6c28a..566664f4 100644
--- a/Numerics/Mathematics/Optimization/Global/ParticleSwarm.cs
+++ b/Numerics/Mathematics/Optimization/Global/ParticleSwarm.cs
@@ -235,7 +235,7 @@ private class Particle
             /// The velocity determines how the particle moves through the search space.
             /// It is updated based on the particle's personal best and the global best.
             /// </remarks>
-            public double[] Velocity { get; set; }
+            public double[] Velocity { get; set; } = null!;
         }
     }
 }
diff --git a/Numerics/Mathematics/Optimization/Local/ADAM.cs b/Numerics/Mathematics/Optimization/Local/ADAM.cs
index 6ba26947..0d8466ec 100644
--- a/Numerics/Mathematics/Optimization/Local/ADAM.cs
+++ b/Numerics/Mathematics/Optimization/Local/ADAM.cs
@@ -76,7 +76,7 @@ public class ADAM : Optimizer
         /// <param name="gradient">Optional. Function to evaluate the gradient. Default uses finite difference.</param>
         public ADAM(Func<double[], double> objectiveFunction, int numberOfParameters,
                     IList<double> initialValues, IList<double> lowerBounds, IList<double> upperBounds, double alpha = 0.001,
-                    Func<double[], double[]> gradient = null) : base(objectiveFunction, numberOfParameters)
+                    Func<double[], double[]> gradient = null!) : base(objectiveFunction, numberOfParameters)
         {
             // Check if the length of the initial, lower and upper bounds equal the number of parameters
             if (initialValues.Count != numberOfParameters || lowerBounds.Count != numberOfParameters || upperBounds.Count != numberOfParameters)
diff --git a/Numerics/Mathematics/Optimization/Local/BFGS.cs b/Numerics/Mathematics/Optimization/Local/BFGS.cs
index 8f76256e..90f949a0 100644
--- a/Numerics/Mathematics/Optimization/Local/BFGS.cs
+++ b/Numerics/Mathematics/Optimization/Local/BFGS.cs
@@ -78,7 +78,7 @@ public class BFGS : Optimizer
         /// <param name="gradient">Optional. Function to evaluate the gradient. Default uses finite difference.</param>
         public BFGS(Func<double[], double> objectiveFunction, int numberOfParameters, 
                     IList<double> initialValues, IList<double> lowerBounds, IList<double> upperBounds, 
-                    Func<double[], double[]> gradient = null) : base(objectiveFunction, numberOfParameters)
+                    Func<double[], double[]> gradient = null!) : base(objectiveFunction, numberOfParameters)
         {
             // Check if the length of the initial, lower and upper bounds equal the number of parameters
             if (initialValues.Count != numberOfParameters || lowerBounds.Count != numberOfParameters || upperBounds.Count != numberOfParameters)
diff --git a/Numerics/Mathematics/Optimization/Local/GradientDescent.cs b/Numerics/Mathematics/Optimization/Local/GradientDescent.cs
index fd4e64d5..d19320af 100644
--- a/Numerics/Mathematics/Optimization/Local/GradientDescent.cs
+++ b/Numerics/Mathematics/Optimization/Local/GradientDescent.cs
@@ -77,7 +77,7 @@ public class GradientDescent : Optimizer
         /// <param name="gradient">Optional. Function to evaluate the gradient. Default uses finite difference.</param>
         public GradientDescent(Func<double[], double> objectiveFunction, int numberOfParameters,
                     IList<double> initialValues, IList<double> lowerBounds, IList<double> upperBounds, double alpha = 0.001,
-                    Func<double[], double[]> gradient = null) : base(objectiveFunction, numberOfParameters)
+                    Func<double[], double[]> gradient = null!) : base(objectiveFunction, numberOfParameters)
         {
             // Check if the length of the initial, lower and upper bounds equal the number of parameters
             if (initialValues.Count != numberOfParameters || lowerBounds.Count != numberOfParameters || upperBounds.Count != numberOfParameters)
diff --git a/Numerics/Mathematics/Optimization/Support/Optimizer.cs b/Numerics/Mathematics/Optimization/Support/Optimizer.cs
index fc22a0ea..f5605d56 100644
--- a/Numerics/Mathematics/Optimization/Support/Optimizer.cs
+++ b/Numerics/Mathematics/Optimization/Support/Optimizer.cs
@@ -60,7 +60,7 @@ protected Optimizer(Func<double[], double> objectiveFunction, int numberOfParame
 
         #region Inputs
 
-        private Func<double[], double> _objectiveFunction;
+        private Func<double[], double> _objectiveFunction = null!;
 
         /// <summary>
         /// The maximum number of optimization iterations allowed. Default = 10,000.
@@ -142,7 +142,7 @@ public Func<double[], double> ObjectiveFunction
         /// <summary>
         /// A trace of the parameter set and fitness evaluated until convergence.
         /// </summary>
-        public List<ParameterSet> ParameterSetTrace { get; protected set; }
+        public List<ParameterSet> ParameterSetTrace { get; protected set; } = null!;
 
         /// <summary>
         /// Determines the optimization method status. 
@@ -152,7 +152,7 @@ public Func<double[], double> ObjectiveFunction
         /// <summary>
         /// The numerically differentiated Hessian matrix. This is only computed when the optimization is successful.
         /// </summary>
-        public Matrix Hessian { get; protected set; }
+        public Matrix Hessian { get; protected set; } = null!;
 
         #endregion
 
@@ -166,7 +166,7 @@ public virtual void ClearResults()
             BestParameterSet = new ParameterSet();
             ParameterSetTrace = new List<ParameterSet>();
             Status = OptimizationStatus.None;
-            Hessian = null;
+            Hessian = null!;
         }
 
         /// <summary>
@@ -287,7 +287,7 @@ protected virtual double RepairParameter(double value, double lowerBound, double
         /// </summary>
         /// <param name="status">Optimization status.</param>
         /// <param name="exception">Inner exception.</param>
-        protected virtual void UpdateStatus(OptimizationStatus status, Exception exception = null)
+        protected virtual void UpdateStatus(OptimizationStatus status, Exception exception = null!)
         {
             Status = status;
             if (status == OptimizationStatus.MaximumIterationsReached)
diff --git a/Numerics/Mathematics/Optimization/Support/ParameterSet.cs b/Numerics/Mathematics/Optimization/Support/ParameterSet.cs
index e154df41..86a61141 100644
--- a/Numerics/Mathematics/Optimization/Support/ParameterSet.cs
+++ b/Numerics/Mathematics/Optimization/Support/ParameterSet.cs
@@ -94,14 +94,17 @@ public ParameterSet(XElement xElement)
                     Values[i] = outVal;
                 }
             }
-            if (xElement.Attribute(nameof(Fitness)) != null)
+
+            var fitAttr = xElement.Attribute(nameof(Fitness));
+            if (fitAttr != null)
             {
-                double.TryParse(xElement.Attribute(nameof(Fitness)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var fitness);
+                double.TryParse(fitAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var fitness);
                 Fitness = fitness;
             }
-            if (xElement.Attribute(nameof(Weight)) != null)
+            var weightAttr = xElement.Attribute(nameof(Weight));
+            if (weightAttr != null)
             {
-                double.TryParse(xElement.Attribute(nameof(Weight)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var weight);
+                double.TryParse(weightAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var weight);
                 Weight = weight;
             }
         }
@@ -109,7 +112,7 @@ public ParameterSet(XElement xElement)
         /// <summary>
         /// The trial parameter set values.
         /// </summary>
-        public double[] Values;
+        public double[] Values = null!;
 
         /// <summary>
         /// The objective function result (or fitness) given the trial parameter set. 
diff --git a/Numerics/Numerics.csproj b/Numerics/Numerics.csproj
index 4c2823b6..380eca32 100644
--- a/Numerics/Numerics.csproj
+++ b/Numerics/Numerics.csproj
@@ -41,7 +41,7 @@
 
 	<ItemGroup>
 		<!--https://github.com/dotnet/reproducible-builds-->
-		<PackageReference Include="DotNet.ReproducibleBuilds" Version="1.2.25">
+		<PackageReference Include="DotNet.ReproducibleBuilds" Version="1.2.39">
 			<PrivateAssets>all</PrivateAssets>
 			<IncludeAssets>runtime; build; native; contentfiles; analyzers</IncludeAssets>
 		</PackageReference>
@@ -64,11 +64,11 @@
     <Reference Include="System.Runtime.Serialization" />
     <Reference Include="System.Xml" />
     <Reference Include="System.Xml.Linq" />
-    <PackageReference Include="System.Text.Encodings.Web" Version="8.0.0" />
-    <PackageReference Include="System.Text.Json" Version="8.0.5" />
-    <PackageReference Include="Microsoft.Bcl.HashCode" Version="1.1.1" />
-    <PackageReference Include="System.Threading.Tasks.Extensions" Version="4.5.4" />
-    <PackageReference Include="System.ValueTuple" Version="4.5.0" />
+    <PackageReference Include="System.Text.Encodings.Web" Version="10.0.1" />
+    <PackageReference Include="System.Text.Json" Version="10.0.1" />
+    <PackageReference Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
+    <PackageReference Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
+    <PackageReference Include="System.ValueTuple" Version="4.6.1" />
   </ItemGroup>
   
   <ItemGroup>
diff --git a/Numerics/Sampling/Bootstrap/Bootstrap.cs b/Numerics/Sampling/Bootstrap/Bootstrap.cs
index b8d694ca..c64db1c7 100644
--- a/Numerics/Sampling/Bootstrap/Bootstrap.cs
+++ b/Numerics/Sampling/Bootstrap/Bootstrap.cs
@@ -44,17 +44,17 @@ public class Bootstrap<TData>
         /// <summary>
         /// Delegate function for resampling the original data and model fit.
         /// </summary>
-        public Func<TData, ParameterSet, Random, TData> ResampleFunction { get; set; }
+        public Func<TData, ParameterSet, Random, TData> ResampleFunction { get; set; } = null!;
 
         /// <summary>
         /// Delegate function for fitting a model.
         /// </summary>
-        public Func<TData, ParameterSet> FitFunction { get; set; }
+        public Func<TData, ParameterSet> FitFunction { get; set; } = null!;
 
         /// <summary>
         /// Delegate function for extracting a statistic from the fit result.
         /// </summary>
-        public Func<ParameterSet, double[]> StatisticFunction { get; set; }
+        public Func<ParameterSet, double[]> StatisticFunction { get; set; } = null!;
 
         /// <summary>
         /// Number of bootstrap replicates.
@@ -79,8 +79,8 @@ public Bootstrap(TData originalData, ParameterSet originalParameters)
 
         private TData _originalData;
         private ParameterSet _originalParameters;
-        private ParameterSet[] _bootstrapParameterSets;
-        private double[][] _bootstrapStatistics;
+        private ParameterSet[] _bootstrapParameterSets = null!;
+        private double[][] _bootstrapStatistics = null!;
 
         /// <summary>
         /// Gets the bootstrapped model parameter sets.
diff --git a/Numerics/Sampling/MCMC/ARWMH.cs b/Numerics/Sampling/MCMC/ARWMH.cs
index 845b4688..59bd92ab 100644
--- a/Numerics/Sampling/MCMC/ARWMH.cs
+++ b/Numerics/Sampling/MCMC/ARWMH.cs
@@ -76,8 +76,8 @@ public ARWMH(List<IUnivariateDistribution> priorDistributions, LogLikelihood log
 
 
         private Matrix sigmaIdentity;
-        private RunningCovarianceMatrix[] sigma;
-        private MultivariateNormal[] mvn;
+        private RunningCovarianceMatrix[] sigma = null!;
+        private MultivariateNormal[] mvn = null!;
 
         /// <summary>
         /// The scaling parameter used to scale the adaptive covariance matrix.
diff --git a/Numerics/Sampling/MCMC/Base/MCMCSampler.cs b/Numerics/Sampling/MCMC/Base/MCMCSampler.cs
index 5135ed16..0c92873a 100644
--- a/Numerics/Sampling/MCMC/Base/MCMCSampler.cs
+++ b/Numerics/Sampling/MCMC/Base/MCMCSampler.cs
@@ -176,17 +176,17 @@ public int ThinningInterval
         /// <summary>
         /// The master pseudo random number generator (PRNG).
         /// </summary>
-        protected Random _masterPRNG;
+        protected Random _masterPRNG = null!;
 
         /// <summary>
         /// The PRNG for each Markov Chain.
         /// </summary>
-        protected Random[] _chainPRNGs;
+        protected Random[] _chainPRNGs = null!;
 
         /// <summary>
         /// The current states of each chain. 
         /// </summary>
-        protected ParameterSet[] _chainStates;
+        protected ParameterSet[] _chainStates = null!;
 
         /// <summary>
         /// The Log-Likelihood function to evaluate. 
@@ -251,12 +251,12 @@ public enum InitializationType
         /// <summary>
         /// The Multivariate Normal proposal distribution set from the MAP estimate.
         /// </summary>
-        protected MultivariateNormal _MVN;
+        protected MultivariateNormal _MVN = null!;
 
         /// <summary>
         /// Event is raised when the simulation progress changes.
         /// </summary>
-        public event ProgressChangedEventHandler ProgressChanged;
+        public event ProgressChangedEventHandler ProgressChanged = null!;
 
         /// <summary>
         /// Event is raised when the simulation progress changes.
@@ -273,7 +273,7 @@ public enum InitializationType
         /// <summary>
         /// Cancellation token source.
         /// </summary>
-        public CancellationTokenSource CancellationTokenSource { get; set; }
+        public CancellationTokenSource CancellationTokenSource { get; set; } = null!;
 
         #endregion
 
@@ -282,22 +282,22 @@ public enum InitializationType
         /// <summary>
         /// Gets the population matrix used for population-based samplers.
         /// </summary>
-        public List<ParameterSet> PopulationMatrix { get; protected set; }
+        public List<ParameterSet> PopulationMatrix { get; protected set; } = null!;
 
         /// <summary>
         /// Gets the list of sampled Markov Chains.
         /// </summary>
-        public List<ParameterSet>[] MarkovChains { get; protected set; }
+        public List<ParameterSet>[] MarkovChains { get; protected set; } = null!;
 
         /// <summary>
         /// Keeps track of the number of accepted samples per chain.
         /// </summary>
-        public int[] AcceptCount { get; protected set; }
+        public int[] AcceptCount { get; protected set; } = null!;
 
         /// <summary>
         /// Keeps track of the number of calls to the proposal sampler per chain.
         /// </summary>
-        public int[] SampleCount { get; protected set; }
+        public int[] SampleCount { get; protected set; } = null!;
 
         /// <summary>
         /// The acceptance rate per chain.
@@ -316,7 +316,7 @@ public double[] AcceptanceRates
         /// <summary>
         /// The average log-likelihood across each chain for each iteration.
         /// </summary>
-        public List<double>  MeanLogLikelihood { get; protected set; }
+        public List<double> MeanLogLikelihood { get; protected set; } = null!;
 
         /// <summary>
         /// Gets and sets the number of posterior parameter sets to output.
@@ -326,7 +326,7 @@ public double[] AcceptanceRates
         /// <summary>
         /// Output posterior parameter sets. These are recorded after the iterations have been completed. 
         /// </summary>
-        public List<ParameterSet>[] Output { get; protected set; }
+        public List<ParameterSet>[] Output { get; protected set; } = null!;
 
         /// <summary>
         /// The output parameter set that produced the maximum likelihood. 
diff --git a/Numerics/Sampling/MCMC/HMC.cs b/Numerics/Sampling/MCMC/HMC.cs
index ad9a125a..36ee7181 100644
--- a/Numerics/Sampling/MCMC/HMC.cs
+++ b/Numerics/Sampling/MCMC/HMC.cs
@@ -80,7 +80,7 @@ public class HMC : MCMCSampler
         /// <param name="stepSize">Optional. The leapfrog step size. Default = 0.1.</param>
         /// <param name="steps">Optional. The number of leapfrog steps. Default = 50.</param>
         /// <param name="gradientFunction">Optional. The function for evaluating the gradient of the log-likelihood. Numerical finite difference will be used by default.</param>
-        public HMC(List<IUnivariateDistribution> priorDistributions, LogLikelihood logLikelihoodFunction, Vector? mass = null, double stepSize = 0.1, int steps = 50, Gradient? gradientFunction = null) : base(priorDistributions, logLikelihoodFunction)
+        public HMC(List<IUnivariateDistribution> priorDistributions, LogLikelihood logLikelihoodFunction, Vector mass = null!, double stepSize = 0.1, int steps = 50, Gradient gradientFunction = null!) : base(priorDistributions, logLikelihoodFunction)
         {
 
             InitialIterations = 100 * NumberOfParameters;
diff --git a/Numerics/Sampling/MCMC/RWMH.cs b/Numerics/Sampling/MCMC/RWMH.cs
index a516ced8..d3801c8c 100644
--- a/Numerics/Sampling/MCMC/RWMH.cs
+++ b/Numerics/Sampling/MCMC/RWMH.cs
@@ -69,7 +69,7 @@ public RWMH(List<IUnivariateDistribution> priorDistributions, LogLikelihood logL
             ProposalSigma = proposalSigma;
         }
 
-        private MultivariateNormal[] mvn;
+        private MultivariateNormal[] mvn = null!;
 
         /// <summary>
         /// The covariance matrix Σ (sigma) for the proposal distribution.
diff --git a/Numerics/Sampling/MCMC/SNIS.cs b/Numerics/Sampling/MCMC/SNIS.cs
index 16acd5b7..5a0561a5 100644
--- a/Numerics/Sampling/MCMC/SNIS.cs
+++ b/Numerics/Sampling/MCMC/SNIS.cs
@@ -59,7 +59,7 @@ public class SNIS : MCMCSampler
         /// <param name="priorDistributions">The list of prior distributions for the model parameters.</param>
         /// <param name="logLikelihoodFunction">The Log-Likelihood function to evaluate.</param>
         /// <param name="multivariateNormal">Optional. The multivariate Normal distribution is used for importance sampling. If null, naive Monte Carlo is performed.</param>
-        public SNIS(List<IUnivariateDistribution> priorDistributions, LogLikelihood logLikelihoodFunction, MultivariateNormal? multivariateNormal = null) : base(priorDistributions, logLikelihoodFunction)
+        public SNIS(List<IUnivariateDistribution> priorDistributions, LogLikelihood logLikelihoodFunction, MultivariateNormal multivariateNormal = null!) : base(priorDistributions, logLikelihoodFunction)
         {
             mvn = multivariateNormal;
             useImportanceSampling = multivariateNormal != null ? true : false;
@@ -75,7 +75,7 @@ public SNIS(List<IUnivariateDistribution> priorDistributions, LogLikelihood logL
         }
 
         private bool useImportanceSampling = false;
-        private MultivariateNormal? mvn = null;
+        private MultivariateNormal mvn = null!;
 
         /// <inheritdoc/>
         protected override void InitializeCustomSettings()
diff --git a/Numerics/Sampling/MCMC/Support/MCMCResults.cs b/Numerics/Sampling/MCMC/Support/MCMCResults.cs
index 9aae090f..7ba6bb2a 100644
--- a/Numerics/Sampling/MCMC/Support/MCMCResults.cs
+++ b/Numerics/Sampling/MCMC/Support/MCMCResults.cs
@@ -94,31 +94,31 @@ public MCMCResults(ParameterSet map, IList<ParameterSet> parameterSets, double a
         /// The list of sampled Markov Chains.
         /// </summary>
         [JsonInclude]
-        public List<ParameterSet>[] MarkovChains { get; private set; }
+        public List<ParameterSet>[] MarkovChains { get; private set; } = null!;
 
         /// <summary>
         /// Output posterior parameter sets.
         /// </summary>
         [JsonInclude]
-        public List<ParameterSet> Output { get; private set; }
+        public List<ParameterSet> Output { get; private set; } = null!;
 
         /// <summary>
         /// The average log-likelihood across each chain for each iteration.
         /// </summary>
         [JsonInclude]
-        public List<double> MeanLogLikelihood { get; private set; }
+        public List<double> MeanLogLikelihood { get; private set; } = null!;
 
         /// <summary>
         /// The acceptance rate for each chain.
         /// </summary>
         [JsonInclude]
-        public double[] AcceptanceRates { get; private set; }
+        public double[] AcceptanceRates { get; private set; } = null!;
 
         /// <summary>
         /// Parameter results using the output posterior parameter sets.
         /// </summary>
         [JsonInclude]
-        public ParameterResults[] ParameterResults { get; private set; }
+        public ParameterResults[] ParameterResults { get; private set; } = null!;
 
         /// <summary>
         /// The output parameter set that produced the maximum likelihood.
@@ -210,7 +210,8 @@ public static MCMCResults FromByteArray(byte[] bytes)
             };
             try
             {
-                return JsonSerializer.Deserialize<MCMCResults>(bytes, options);
+                return JsonSerializer.Deserialize<MCMCResults>(bytes, options)
+                    ?? throw new JsonException("Deserialized MCMCResults was null");
             }
             catch
             {
diff --git a/Numerics/Sampling/MCMC/Support/ParameterResults.cs b/Numerics/Sampling/MCMC/Support/ParameterResults.cs
index c96c7fa8..7ac720a0 100644
--- a/Numerics/Sampling/MCMC/Support/ParameterResults.cs
+++ b/Numerics/Sampling/MCMC/Support/ParameterResults.cs
@@ -102,7 +102,7 @@ public ParameterResults(double[] values, double alpha = 0.1, bool sorted = false
         /// <summary>
         /// The autocorrelation function for each parameter. This is averaged across each chain.
         /// </summary>
-        public double[,] Autocorrelation { get; set; }
+        public double[,] Autocorrelation { get; set; } = null!;
 
     }
 }
diff --git a/Numerics/Sampling/SobolSequence.cs b/Numerics/Sampling/SobolSequence.cs
index c8ea8c39..4f6b753e 100644
--- a/Numerics/Sampling/SobolSequence.cs
+++ b/Numerics/Sampling/SobolSequence.cs
@@ -136,8 +136,8 @@ private void initialize()
                 reader.ReadLine();
 
                 int index = 1;
-                string line = null;
-                while ((line = reader.ReadLine()) != null)
+                string? line;
+                while ((line = reader.ReadLine()) is not null)
                 {
                     var st = line.Split(' ');
 
diff --git a/Numerics/Sampling/StratificationBin.cs b/Numerics/Sampling/StratificationBin.cs
index 0cfc3c64..db403ae7 100644
--- a/Numerics/Sampling/StratificationBin.cs
+++ b/Numerics/Sampling/StratificationBin.cs
@@ -83,21 +83,24 @@ public StratificationBin(double lowerBound, double upperBound, double weight = -
         public StratificationBin(XElement element)
         {
             // Get required data
-            if (element.Attribute(nameof(LowerBound)) != null)
+            var lowerBoundAttr = element.Attribute(nameof(LowerBound));
+            if (lowerBoundAttr != null)
             {
-                double.TryParse(element.Attribute(nameof(LowerBound)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var lower);
+                double.TryParse(lowerBoundAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var lower);
                 LowerBound = lower;
             }
 
-            if (element.Attribute(nameof(UpperBound)) != null)
+            var upperBoundAttr = element.Attribute(nameof(UpperBound));
+            if (upperBoundAttr != null)
             {
-                double.TryParse(element.Attribute(nameof(UpperBound)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var upper);
+                double.TryParse(upperBoundAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var upper);
                 UpperBound = upper;
             }
 
-            if (element.Attribute(nameof(Weight)) != null)
+            var weightAttr = element.Attribute(nameof(Weight));
+            if (weightAttr != null)
             {
-                double.TryParse(element.Attribute(nameof(Weight)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var weight);
+                double.TryParse(weightAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var weight);
                 Weight = weight;
             }
         }
@@ -142,8 +145,11 @@ public bool Contains(double x)
         /// +1 if this bin is lower than the compared bin.
         /// -1 otherwise.
         /// </returns>
-        public int CompareTo(StratificationBin other)
+        public int CompareTo(StratificationBin? other)
         {
+            if (other == null)
+                throw new ArgumentNullException(nameof(other), "The stratification bin to compare to cannot be null.");
+
             if (UpperBound > other.LowerBound && LowerBound < other.UpperBound)
                 throw new ArgumentException("The bins cannot be overlapping.", nameof(other));
 
@@ -164,7 +170,7 @@ public object Clone()
         /// <summary>
         /// Checks whether two stratification bins are equal.
         /// </summary>
-        public override bool Equals(object obj)
+        public override bool Equals(object? obj)
         {
             if (!(obj is StratificationBin))
                 return false;
diff --git a/Numerics/Sampling/StratificationOptions.cs b/Numerics/Sampling/StratificationOptions.cs
index 94c1a45b..daf1032d 100644
--- a/Numerics/Sampling/StratificationOptions.cs
+++ b/Numerics/Sampling/StratificationOptions.cs
@@ -98,27 +98,31 @@ public StratificationOptions(double lowerBound, double upperBound, int numberOfB
         public StratificationOptions(XElement element)
         {
             // Get required data
-            if (element.Attribute(nameof(LowerBound)) != null)
+            var lowerBoundAttr = element.Attribute(nameof(LowerBound));
+            if (lowerBoundAttr != null)
             {
-                double.TryParse(element.Attribute(nameof(LowerBound)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var lower);
+                double.TryParse(lowerBoundAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var lower);
                 LowerBound = lower;
             }
 
-            if (element.Attribute(nameof(UpperBound)) != null)
+            var upperBoundAttr = element.Attribute(nameof(UpperBound));
+            if (upperBoundAttr != null)
             {
-                double.TryParse(element.Attribute(nameof(UpperBound)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var upper);
+                double.TryParse(upperBoundAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var upper);
                 UpperBound = upper;
             }
 
-            if (element.Attribute(nameof(NumberOfBins)) != null)
+            var numberOfBinsAttr = element.Attribute(nameof(NumberOfBins));
+            if (numberOfBinsAttr != null)
             {
-                int.TryParse(element.Attribute(nameof(NumberOfBins)).Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var nBins);
+                int.TryParse(numberOfBinsAttr.Value, NumberStyles.Any, CultureInfo.InvariantCulture, out var nBins);
                 NumberOfBins = nBins;
             }
 
-            if (element.Attribute(nameof(IsProbability)) != null)
+            var isProbabilityAttr = element.Attribute(nameof(IsProbability));
+            if (isProbabilityAttr != null)
             {
-                bool.TryParse(element.Attribute(nameof(IsProbability)).Value, out var isProbability);
+                bool.TryParse(isProbabilityAttr.Value, out var isProbability);
                 IsProbability = isProbability;
             }
 
@@ -228,7 +232,7 @@ private void Validate()
         /// <summary>
         /// Compares objects for equality.
         /// </summary>
-        public override bool Equals(object obj)
+        public override bool Equals(object? obj)
         {
             if (obj is not StratificationOptions other)
                 return false;
diff --git a/Numerics/Sampling/Stratify.cs b/Numerics/Sampling/Stratify.cs
index 676b540a..2519d910 100644
--- a/Numerics/Sampling/Stratify.cs
+++ b/Numerics/Sampling/Stratify.cs
@@ -302,7 +302,7 @@ public static List<StratificationBin> Probabilities(StratificationOptions option
         /// <param name="dimension">The number of dimensions to stratify.</param>
         /// <param name="seed"> Seed for random number generator. </param>
         /// <param name="correlation">The correlation matrix. If null, independence is assumed.</param>
-        public static List<List<StratificationBin>> MultivariateProbabilities(StratificationOptions options, ImportanceDistribution distributionType = ImportanceDistribution.Uniform, bool isExhaustive = true, int dimension = 1, int seed = -1, double[,] correlation = null)
+        public static List<List<StratificationBin>> MultivariateProbabilities(StratificationOptions options, ImportanceDistribution distributionType = ImportanceDistribution.Uniform, bool isExhaustive = true, int dimension = 1, int seed = -1, double[,] correlation = null!)
         {
             // Validate inputs
             var output = new List<List<StratificationBin>>();
diff --git a/Numerics/Utilities/ExtensionMethods.cs b/Numerics/Utilities/ExtensionMethods.cs
index c83f06bc..e9f66ad9 100644
--- a/Numerics/Utilities/ExtensionMethods.cs
+++ b/Numerics/Utilities/ExtensionMethods.cs
@@ -63,7 +63,7 @@ public static T GetAttributeOfType<T>(this Enum enumValue) where T : Attribute
             var type = enumValue.GetType();
             var memInfo = type.GetMember(enumValue.ToString());
             var attributes = memInfo[0].GetCustomAttributes(typeof(T), false);
-            return (attributes.Length > 0) ? (T)attributes[0] : null;
+            return (attributes.Length > 0) ? (T)attributes[0] : null!;
         }
 
         #endregion
diff --git a/Numerics/Utilities/JsonConverters.cs b/Numerics/Utilities/JsonConverters.cs
index 79877aaa..cc079278 100644
--- a/Numerics/Utilities/JsonConverters.cs
+++ b/Numerics/Utilities/JsonConverters.cs
@@ -78,14 +78,14 @@ public class Double2DArrayConverter : JsonConverter<double[,]>
         public override double[,] Read(ref Utf8JsonReader reader, Type typeToConvert, JsonSerializerOptions options)
         {
             if (reader.TokenType == JsonTokenType.Null)
-                return null;
+                return null!;
 
             if (reader.TokenType != JsonTokenType.StartObject)
                 throw new JsonException("Expected StartObject token");
 
             int rows = 0;
             int cols = 0;
-            double[] data = null;
+            double[]? data = null!;
 
             while (reader.Read())
             {
@@ -94,7 +94,7 @@ public class Double2DArrayConverter : JsonConverter<double[,]>
 
                 if (reader.TokenType == JsonTokenType.PropertyName)
                 {
-                    string propertyName = reader.GetString();
+                    string? propertyName = reader.GetString();
                     reader.Read();
 
                     switch (propertyName)
@@ -217,14 +217,14 @@ public class String2DArrayConverter : JsonConverter<string[,]>
         public override string[,] Read(ref Utf8JsonReader reader, Type typeToConvert, JsonSerializerOptions options)
         {
             if (reader.TokenType == JsonTokenType.Null)
-                return null;
+                return null!;
 
             if (reader.TokenType != JsonTokenType.StartObject)
                 throw new JsonException("Expected StartObject token");
 
             int rows = 0;
             int cols = 0;
-            string[] data = null;
+            string[]? data = null!;
 
             while (reader.Read())
             {
@@ -233,7 +233,7 @@ public class String2DArrayConverter : JsonConverter<string[,]>
 
                 if (reader.TokenType == JsonTokenType.PropertyName)
                 {
-                    string propertyName = reader.GetString();
+                    string? propertyName = reader.GetString();
                     reader.Read();
 
                     switch (propertyName)
@@ -371,13 +371,13 @@ public class UnivariateDistributionConverter : JsonConverter<UnivariateDistribut
         public override UnivariateDistributionBase Read(ref Utf8JsonReader reader, Type typeToConvert, JsonSerializerOptions options)
         {
             if (reader.TokenType == JsonTokenType.Null)
-                return null;
+                return null!;
 
             if (reader.TokenType != JsonTokenType.StartObject)
                 throw new JsonException("Expected StartObject token");
 
             UnivariateDistributionType? distributionType = null;
-            double[] parameters = null;
+            double[]? parameters = null!;
 
             while (reader.Read())
             {
@@ -386,7 +386,7 @@ public override UnivariateDistributionBase Read(ref Utf8JsonReader reader, Type
 
                 if (reader.TokenType == JsonTokenType.PropertyName)
                 {
-                    string propertyName = reader.GetString();
+                    string? propertyName = reader.GetString();
                     reader.Read();
 
                     switch (propertyName)
@@ -402,22 +402,22 @@ public override UnivariateDistributionBase Read(ref Utf8JsonReader reader, Type
             }
 
             if (!distributionType.HasValue || parameters == null)
-                return null;
+                return null!;
 
             // Use the factory to create a default distribution, then set its parameters
             try
             {
                 var distribution = UnivariateDistributionFactory.CreateDistribution(distributionType.Value);
-                if (distribution != null && parameters != null && parameters.Length > 0)
+                if (distribution != null! && parameters != null && parameters.Length > 0)
                 {
                     distribution.SetParameters(parameters);
                 }
-                return distribution;
+                return distribution!;
             }
             catch
             {
                 // If we can't recreate it, return null
-                return null;
+                return null!;
             }
         }
 
@@ -443,7 +443,7 @@ public override UnivariateDistributionBase Read(ref Utf8JsonReader reader, Type
         /// </remarks>
         public override void Write(Utf8JsonWriter writer, UnivariateDistributionBase value, JsonSerializerOptions options)
         {
-            if (value == null)
+            if (value == null!)
             {
                 writer.WriteNullValue();
                 return;
diff --git a/Numerics/Utilities/SafeProgressReporter.cs b/Numerics/Utilities/SafeProgressReporter.cs
index 2e1c4973..b2817074 100644
--- a/Numerics/Utilities/SafeProgressReporter.cs
+++ b/Numerics/Utilities/SafeProgressReporter.cs
@@ -79,12 +79,12 @@ public SafeProgressReporter(string taskName)
         private double _previousProgress = -0.0000000000001d;
         private string _previousMessage = "";
         private MessageType _previousMessageType = MessageType.Status;
-        private Process _externalProcess;
+        private Process _externalProcess = null!;
         private List<SafeProgressReporter> _subProgReporterCollection = new List<SafeProgressReporter>();
         private CancellationTokenSource _cancellationTokenSource = new CancellationTokenSource();
         protected readonly SendOrPostCallback _invokeProgressHandlers;
         protected readonly SendOrPostCallback _invokeMessageHandlers;
-        protected SynchronizationContext _synchronizationContext;
+        protected SynchronizationContext? _synchronizationContext;
 
         /// <summary>
         /// Returns the most recent progress.
@@ -104,7 +104,7 @@ public SafeProgressReporter(string taskName)
         /// <summary>
         /// Returns the task name.
         /// </summary>
-        public string TaskName { get; private set; }
+        public string TaskName { get; private set; } = null!;
 
         /// <summary>
         /// Returns the most recent message type.
@@ -132,7 +132,7 @@ public ReadOnlyCollection<SafeProgressReporter> ChildReporters
         /// <summary>
         /// Event is raised when the progress is reported.
         /// </summary>
-        public event ProgressReportedEventHandler ProgressReported;
+        public event ProgressReportedEventHandler ProgressReported = null!;
 
         /// <summary>
         /// Delegate for handling progress reported events.
@@ -145,7 +145,7 @@ public ReadOnlyCollection<SafeProgressReporter> ChildReporters
         /// <summary>
         /// Event is raised when a message is reported.
         /// </summary>
-        public event MessageReportedEventHandler MessageReported;
+        public event MessageReportedEventHandler MessageReported = null!;
 
         /// <summary>
         /// Delegate for handling message reported events.
@@ -156,7 +156,7 @@ public ReadOnlyCollection<SafeProgressReporter> ChildReporters
         /// <summary>
         /// Event is raised when the task starts.
         /// </summary>
-        public event TaskStartedEventHandler TaskStarted;
+        public event TaskStartedEventHandler TaskStarted = null!;
 
         /// <summary>
         /// Delegate for handling task started events.
@@ -166,7 +166,7 @@ public ReadOnlyCollection<SafeProgressReporter> ChildReporters
         /// <summary>
         /// Event is raised when the task ended.
         /// </summary>
-        public event TaskEndedEventHandler TaskEnded;
+        public event TaskEndedEventHandler TaskEnded = null!;
 
         /// <summary>
         /// Delegate for handling task ended events.
@@ -176,7 +176,7 @@ public ReadOnlyCollection<SafeProgressReporter> ChildReporters
         /// <summary>
         /// Event is raised when a child reporter is created.
         /// </summary>
-        public event ChildReporterCreatedEventHandler ChildReporterCreated;
+        public event ChildReporterCreatedEventHandler ChildReporterCreated = null!;
 
         /// <summary>
         /// Delegate for handling child reporter created events.
@@ -287,9 +287,9 @@ public void ReportError(string message)
         /// Invokes the progress handlers.
         /// </summary>
         /// <param name="state">The object.</param>
-        private void InvokeProgressHandlers(object state)
+        private void InvokeProgressHandlers(object? state)
         {
-            double prog = ((double[])state)[0];
+            double prog = ((double[])state!)[0];
             double prevProg = ((double[])state)[1];
             if (prevProg < 0d)
                 prevProg = 0d;
@@ -301,9 +301,9 @@ private void InvokeProgressHandlers(object state)
         /// Invokes the message handlers.
         /// </summary>
         /// <param name="state">The object.</param>
-        private void InvokeMessageHandlers(object state)
+        private void InvokeMessageHandlers(object? state)
         {
-            MessageContentStruct prog = (MessageContentStruct)state;
+            MessageContentStruct prog = (MessageContentStruct)state!;
             OnMessageReported(prog);
             MessageReported?.Invoke(prog);
         }
@@ -353,7 +353,7 @@ public SafeProgressReporter CreateProgressModifier(float fractionOfTotal, string
             if (string.IsNullOrEmpty(subTaskName))
                 subTaskName = TaskName;
             var child = new SafeProgressReporter(subTaskName);
-            child.SetContext(_synchronizationContext);
+            child.SetContext(_synchronizationContext!);
             child._previousProgress = 0d;
             child.ProgressReported += (reporter, prog, progDelta) => ReportProgress(_previousProgress + progDelta * fractionOfTotal);
             child.MessageReported += msg => ReportMessage(msg);
diff --git a/Numerics/Utilities/Tools.cs b/Numerics/Utilities/Tools.cs
index 9479887e..5cad3c99 100644
--- a/Numerics/Utilities/Tools.cs
+++ b/Numerics/Utilities/Tools.cs
@@ -783,7 +783,7 @@ public static double[] Sequence(double start, double end, double step = 1)
         /// <param name="data">An array of bytes.</param>
         public static byte[] Compress(byte[] data)
         {
-            if (data is null) return null;
+            if (data is null) return null!;
             var output = new MemoryStream();
             using (var dstream = new DeflateStream(output, CompressionLevel.Optimal))
             {
@@ -798,7 +798,7 @@ public static byte[] Compress(byte[] data)
         /// <param name="data">An array of bytes.</param>
         public static byte[] Decompress(byte[] data)
         {
-            if (data is null) return null;
+            if (data is null) return null!;
             var input = new MemoryStream(data);
             var output = new MemoryStream();
             using (var dstream = new DeflateStream(input, CompressionMode.Decompress))
diff --git a/Test_Numerics/Data/Interpolation/Test_Bilinear.cs b/Test_Numerics/Data/Interpolation/Test_Bilinear.cs
index 0c710915..540ff239 100644
--- a/Test_Numerics/Data/Interpolation/Test_Bilinear.cs
+++ b/Test_Numerics/Data/Interpolation/Test_Bilinear.cs
@@ -93,7 +93,7 @@ public void Test_BiLinear()
             double x1 = 350d;
             double x2 = 75d;
             double y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 874.84d, 1E-6);
+            Assert.AreEqual(874.84d, y,  1E-6);
         }
 
         /// <summary>
@@ -136,31 +136,31 @@ public void Test_Log()
 
             var LogLinLin = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.Logarithmic };
             double y1 = LogLinLin.Interpolate(x1, x2);
-            Assert.AreEqual(y1, 874.909523653025d, 1E-6);
+            Assert.AreEqual(874.909523653025d, y1,  1E-6);
 
             var LinLogLin = new Bilinear(x1Array, x2Array, yArray) { X2Transform = Transform.Logarithmic };
             double y2 = LinLogLin.Interpolate(x1, x2);
-            Assert.AreEqual(y2, 875.919023759159d, 1E-6);
+            Assert.AreEqual(875.919023759159d, y2, 1E-6);
 
             var LinLinLog = new Bilinear(x1Array, x2Array, yArray) { YTransform = Transform.Logarithmic };
             double y3 = LinLinLog.Interpolate(x1, x2);
-            Assert.AreEqual(y3, 874.8164, 1E-4);
+            Assert.AreEqual(874.8164, y3,  1E-4);
 
             var LinLogLog = new Bilinear(x1Array, x2Array, yArray) { X2Transform = Transform.Logarithmic, YTransform = Transform.Logarithmic };
             double y4 = LinLogLog.Interpolate(x1, x2);
-            Assert.AreEqual(y4, 875.896104342695d, 1E-6);
+            Assert.AreEqual(875.896104342695d, y4, 1E-6);
 
             var LogLogLin = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.Logarithmic, X2Transform = Transform.Logarithmic };
             double y5 = LogLogLin.Interpolate(x1, x2);
-            Assert.AreEqual(y5, 875.9855, 1E-4);
+            Assert.AreEqual(875.9855, y5, 1E-4);
 
             var LogLinLog = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.Logarithmic, YTransform = Transform.Logarithmic };
             double y6 = LogLinLog.Interpolate(x1, x2);
-            Assert.AreEqual(y6, 874.886, 1E-4);
+            Assert.AreEqual(874.886, y6, 1E-4);
 
             var LogLogLog = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.Logarithmic, X2Transform = Transform.Logarithmic, YTransform = Transform.Logarithmic };
             double y7 = LogLogLog.Interpolate(x1, x2);
-            Assert.AreEqual(y7, 875.962713889793d, 1E-6);
+            Assert.AreEqual(875.962713889793d, y7, 1E-6);
         }
 
 
@@ -204,15 +204,15 @@ public void Test_Z_X()
 
             var ZLinLin = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.NormalZ };
             double y1 = ZLinLin.Interpolate(x1, x2);
-            Assert.AreEqual(y1, 890.8358, 1E-4);
+            Assert.AreEqual(890.8358, y1, 1E-4);
 
             var LinZLin = new Bilinear(x1Array, x2Array, yArray) { X2Transform = Transform.NormalZ };
             double y2 = LinZLin.Interpolate(x1, x2);
-            Assert.AreEqual(y2, 890.7267, 1E-4);
+            Assert.AreEqual(890.7267, y2, 1E-4);
 
             var ZZLin = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.NormalZ, X2Transform = Transform.NormalZ };
             double y3 = ZZLin.Interpolate(x1, x2);
-            Assert.AreEqual(y3, 890.6835, 1E-4);
+            Assert.AreEqual(890.6835, y3, 1E-4);
         }
 
         /// <summary>
@@ -255,19 +255,19 @@ public void Test_Z_Y()
 
             var LinLinZ = new Bilinear(x1Array, x2Array, yArray) { YTransform = Transform.NormalZ };
             double y1 = LinLinZ.Interpolate(x1, x2);
-            Assert.AreEqual(y1, 0.9596228, 1E-6);
+            Assert.AreEqual(0.9596228, y1, 1E-6);
 
             var LinZZ = new Bilinear(x1Array, x2Array, yArray) { X2Transform = Transform.NormalZ, YTransform = Transform.NormalZ };
             double y2 = LinZZ.Interpolate(x1, x2);
-            Assert.AreEqual(y2, 0.95946, 1E-6);
+            Assert.AreEqual(0.95946, y2, 1E-6);
 
             var ZLinZ = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.NormalZ, YTransform = Transform.NormalZ };
             double y3 = ZLinZ.Interpolate(x1, x2);
-            Assert.AreEqual(y3, 0.9595799, 1E-6);
+            Assert.AreEqual(0.9595799, y3, 1E-6);
 
             var ZZZ = new Bilinear(x1Array, x2Array, yArray) { X1Transform = Transform.NormalZ, X2Transform = Transform.NormalZ, YTransform = Transform.NormalZ };
             double y4 = ZZZ.Interpolate(x1, x2);
-            Assert.AreEqual(y4, 0.9594168, 1E-6);
+            Assert.AreEqual(0.9594168, y4, 1E-6);
         }
 
         /// <summary>
@@ -311,46 +311,46 @@ public void Test_BilinearEdgeCases()
             double x1 = 50;
             double x2 = 28;
             double y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 850.36, 1E-6);
+            Assert.AreEqual(850.36, y, 1E-6);
             // Top Right
             x1 = 50;
             x2 = 300;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 928.87, 1E-6);
+            Assert.AreEqual(928.87, y, 1E-6);
             // Bottom Left
             x1 = 600;
             x2 = 25;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 871.84, 1E-6);
+            Assert.AreEqual(871.84, y, 1E-6);
             // Bottom Right
             x1 = 600;
             x2 = 300;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 929.68, 1E-6);
+            Assert.AreEqual(929.68, y, 1E-6);
 
             // Ascending - x1 out
             // Top
             x1 = 50;
             x2 = 75;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 859.405, 1E-6);
+            Assert.AreEqual(859.405, y, 1E-6);
             // Bottom
             x1 = 600;
             x2 = 225;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 924.93, 1E-6);
+            Assert.AreEqual(924.93, y, 1E-6);
 
             // Ascending - x2 out
             // Top
             x1 = 125;
             x2 = 25;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 854.11750, 1E-6);
+            Assert.AreEqual(854.11750, y, 1E-6);
             // Bottom
             x1 = 450;
             x2 = 300;
             y = bilinear.Interpolate(x1, x2);
-            Assert.AreEqual(y, 929.65000, 1E-6);
+            Assert.AreEqual(929.65000, y, 1E-6);
 
         }
     }
diff --git a/Test_Numerics/Data/Interpolation/Test_CubicSpline.cs b/Test_Numerics/Data/Interpolation/Test_CubicSpline.cs
index 18f1e3b1..51faa09a 100644
--- a/Test_Numerics/Data/Interpolation/Test_CubicSpline.cs
+++ b/Test_Numerics/Data/Interpolation/Test_CubicSpline.cs
@@ -66,12 +66,12 @@ public void Test_Sequential()
                 values[i - 1] = i;
             var spline = new CubicSpline(values, values);
             var lo = spline.SequentialSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871,lo);
 
             Array.Reverse(values);
             var spline2 = new CubicSpline(values, values, SortOrder.Descending);
             lo = spline2.SequentialSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
         }
 
         /// <summary>
@@ -85,12 +85,12 @@ public void Test_Bisection()
                 values[i - 1] = i;
             var spline = new CubicSpline(values, values);
             var lo = spline.BisectionSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
 
             Array.Reverse(values);
             var spline2 = new CubicSpline(values, values, SortOrder.Descending);
             lo = spline2.BisectionSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127,lo);
         }
 
         /// <summary>
@@ -104,12 +104,12 @@ public void Test_Hunt()
                 values[i - 1] = i;
             var spline = new CubicSpline(values, values);
             var lo = spline.HuntSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871,lo);
 
             Array.Reverse(values);
             var spline2 = new CubicSpline(values, values, SortOrder.Descending);
             lo = spline2.HuntSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
         }
 
         /// <summary>
@@ -123,7 +123,7 @@ public void Test_Interpolate()
             var spline = new CubicSpline(XArray, YArray);
             double X = 8d;
             double Y = spline.Interpolate(X);
-            Assert.AreEqual(Y, 11.4049889205445d, 1E-6);
+            Assert.AreEqual(11.4049889205445d, Y,  1E-6);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Data/Interpolation/Test_Linear.cs b/Test_Numerics/Data/Interpolation/Test_Linear.cs
index f126a074..646c28c3 100644
--- a/Test_Numerics/Data/Interpolation/Test_Linear.cs
+++ b/Test_Numerics/Data/Interpolation/Test_Linear.cs
@@ -65,12 +65,12 @@ public void Test_Sequential()
                 values[i - 1] = i;
             var LI = new Linear(values, values);
             var lo = LI.SequentialSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871,lo);
 
             Array.Reverse(values);
             LI = new Linear(values, values, SortOrder.Descending);
             lo = LI.SequentialSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127,lo);
         }
 
         /// <summary>
@@ -84,12 +84,12 @@ public void Test_Bisection()
                 values[i - 1] = i;
             var LI = new Linear(values, values);
             var lo = LI.BisectionSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
 
             Array.Reverse(values);
             LI = new Linear(values, values, SortOrder.Descending);
             lo = LI.BisectionSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
         }
 
         /// <summary>
@@ -103,12 +103,12 @@ public void Test_Hunt()
                 values[i - 1] = i;
             var LI = new Linear(values, values);
             var lo = LI.HuntSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
 
             Array.Reverse(values);
             LI = new Linear(values, values, SortOrder.Descending);
             lo = LI.HuntSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
         }
 
         /// <summary>
@@ -122,7 +122,7 @@ public void Test_Lin()
             var LI = new Linear(XArray, YArray);
             double X = 75d;
             double Y = LI.Interpolate(X);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y,  1E-6);
         }
 
         /// <summary>
@@ -155,15 +155,15 @@ public void Test_Log()
 
             var LinLog = new Linear(XArray, YArray) { YTransform = Transform.Logarithmic };
             double Y1 = LinLog.Interpolate(X);
-            Assert.AreEqual(Y1, 141.42135623731d, 1E-6);
+            Assert.AreEqual(141.42135623731d, Y1,  1E-6);
 
             var LogLin = new Linear(XArray, YArray) { XTransform = Transform.Logarithmic };
             double Y2 = LogLin.Interpolate(X);
-            Assert.AreEqual(Y2, 158.496250072116d, 1E-6);
+            Assert.AreEqual(158.496250072116d, Y2,  1E-6);
 ;
             var LogLog = new Linear(XArray, YArray) { XTransform = Transform.Logarithmic, YTransform = Transform.Logarithmic };
             double Y3 = LogLog.Interpolate(X);
-            Assert.AreEqual(Y3, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y3,  1E-6);
         }
 
         /// <summary>
@@ -214,15 +214,15 @@ public void Test_Z()
 
             var LinZ = new Linear(XArray, YArray) { YTransform = Transform.NormalZ };
             double Y1 = LinZ.Interpolate(X);
-            Assert.AreEqual(Y1, 0.358762529d, 1E-6);
+            Assert.AreEqual(0.358762529d, Y1,  1E-6);
 
             var ZLin = new Linear(XArray, YArray) { XTransform = Transform.NormalZ };
             double Y2 = ZLin.Interpolate(X);
-            Assert.AreEqual(Y2, 0.362146174d, 1E-6);
+            Assert.AreEqual(0.362146174d, Y2,  1E-6);
 
             var ZZ = new Linear(XArray, YArray) { XTransform = Transform.NormalZ, YTransform = Transform.NormalZ };
             double Y3 = ZZ.Interpolate(X);
-            Assert.AreEqual(Y3, 0.36093855992815d, 1E-6);
+            Assert.AreEqual(0.36093855992815d, Y3,  1E-6);
         }
 
         /// <summary>
@@ -274,7 +274,7 @@ public void Test_RevLin()
             var LI = new Linear(XArray, YArray, SortOrder.Descending);
             double X = 75d;
             double Y = LI.Interpolate(X);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y,  1E-6);
         }
 
         /// <summary>
@@ -292,15 +292,15 @@ public void Test_Rev_Log()
 
             var LinLog = new Linear(XArray, YArray, SortOrder.Descending) { YTransform = Transform.Logarithmic };
             double Y1 = LinLog.Interpolate(X);
-            Assert.AreEqual(Y1, 141.42135623731d, 1E-6);
+            Assert.AreEqual(141.42135623731d, Y1,  1E-6);
 
             var LogLin = new Linear(XArray, YArray, SortOrder.Descending) { XTransform = Transform.Logarithmic };
             double Y2 = LogLin.Interpolate(X);
-            Assert.AreEqual(Y2, 158.496250072116d, 1E-6);
+            Assert.AreEqual(158.496250072116d, Y2,  1E-6);
 
             var LogLog = new Linear(XArray, YArray, SortOrder.Descending) { XTransform = Transform.Logarithmic, YTransform = Transform.Logarithmic };
             double Y3 = LogLog.Interpolate(X);
-            Assert.AreEqual(Y3, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y3, 1E-6);
         }
 
         /// <summary>
@@ -318,15 +318,15 @@ public void Test_Rev_Z()
 
             var LinZ = new Linear(XArray, YArray, SortOrder.Descending) { YTransform = Transform.NormalZ };
             double Y1 = LinZ.Interpolate(X);
-            Assert.AreEqual(Y1, 0.358762529d, 1E-6);
+            Assert.AreEqual(0.358762529d, Y1, 1E-6);
 
             var ZLin = new Linear(XArray, YArray, SortOrder.Descending) { XTransform = Transform.NormalZ };
             double Y2 = ZLin.Interpolate(X);
-            Assert.AreEqual(Y2, 0.362146174d, 1E-6);
+            Assert.AreEqual(0.362146174d, Y2, 1E-6);
 
             var ZZ = new Linear(XArray, YArray, SortOrder.Descending) { XTransform = Transform.NormalZ, YTransform = Transform.NormalZ };
             double Y3 = ZZ.Interpolate(X);
-            Assert.AreEqual(Y3, 0.36093855992815d, 1E-6);
+            Assert.AreEqual(0.36093855992815d, Y3, 1E-6);
         }
 
         // ???
diff --git a/Test_Numerics/Data/Interpolation/Test_Polynomial.cs b/Test_Numerics/Data/Interpolation/Test_Polynomial.cs
index f9c291fc..be9e710e 100644
--- a/Test_Numerics/Data/Interpolation/Test_Polynomial.cs
+++ b/Test_Numerics/Data/Interpolation/Test_Polynomial.cs
@@ -66,12 +66,12 @@ public void Test_Sequential()
                 values[i - 1] = i;
             var poly = new Polynomial(3, values, values);
             var lo = poly.SequentialSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo );
 
             Array.Reverse(values);
             var poly2 = new Polynomial(3, values, values, SortOrder.Descending);
             lo = poly2.SequentialSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127,lo);
         }
 
         /// <summary>
@@ -85,12 +85,12 @@ public void Test_Bisection()
                 values[i - 1] = i;
             var poly = new Polynomial(3, values, values);
             var lo = poly.BisectionSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871,lo);
 
             Array.Reverse(values);
             var poly2 = new Polynomial(3, values, values, SortOrder.Descending);
             lo = poly2.BisectionSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127,lo);
         }
 
         /// <summary>
@@ -104,12 +104,12 @@ public void Test_Hunt()
                 values[i - 1] = i;
             var poly = new Polynomial(3, values, values);
             var lo = poly.HuntSearch(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871,lo);
 
             Array.Reverse(values);
             var poly2 = new Polynomial(3, values, values, SortOrder.Descending);
             lo = poly2.HuntSearch(872.5);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127,lo);
         }
 
         /// <summary>
@@ -123,7 +123,7 @@ public void Test_Interpolate_Order3()
             var poly = new Polynomial(3, XArray, YArray);
             double X = 8d;
             double Y = poly.Interpolate(X);
-            Assert.AreEqual(Y, 11.5415808882467, 1E-6);
+            Assert.AreEqual(11.5415808882467, Y,  1E-6);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Data/Paired Data/Test_Ordinate.cs b/Test_Numerics/Data/Paired Data/Test_Ordinate.cs
index 2bcb3f4d..a3437a3a 100644
--- a/Test_Numerics/Data/Paired Data/Test_Ordinate.cs	
+++ b/Test_Numerics/Data/Paired Data/Test_Ordinate.cs	
@@ -71,14 +71,14 @@ public void Test_Construction()
             var ordinate4 = new Ordinate(double.NaN, 4);
 
             Assert.AreEqual(ordinate1, ordinate2);
-            Assert.AreEqual(ordinate1.X, 2);
-            Assert.AreEqual(ordinate1.Y, 4);
-            Assert.AreEqual(ordinate2.X, 2);
-            Assert.AreEqual(ordinate2.Y, 4);
+            Assert.AreEqual(2,ordinate1.X);
+            Assert.AreEqual(4, ordinate1.Y);
+            Assert.AreEqual(2, ordinate2.X);
+            Assert.AreEqual(4, ordinate2.Y);
             
-            Assert.AreEqual(ordinate1.IsValid, true);
-            Assert.AreEqual(ordinate3.IsValid, false);
-            Assert.AreEqual(ordinate4.IsValid, false);
+            Assert.IsTrue(ordinate1.IsValid);
+            Assert.IsFalse(ordinate3.IsValid);
+            Assert.IsFalse(ordinate4.IsValid);
 
             Assert.AreNotEqual(ordinate1, ordinate3);
 
diff --git a/Test_Numerics/Data/Paired Data/Test_PairedDataInterpolation.cs b/Test_Numerics/Data/Paired Data/Test_PairedDataInterpolation.cs
index 8f198375..5ff08e81 100644
--- a/Test_Numerics/Data/Paired Data/Test_PairedDataInterpolation.cs	
+++ b/Test_Numerics/Data/Paired Data/Test_PairedDataInterpolation.cs	
@@ -56,10 +56,10 @@ public void Test_Sequential()
                 opd.Add(new Ordinate(i, i));
             // X
             var lo = opd.SequentialSearchX(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871,lo);
             // Y
             lo = opd.SequentialSearchY(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
 
             // DSC
             opd = new OrderedPairedData(true, SortOrder.Descending, false, SortOrder.Descending);
@@ -67,10 +67,10 @@ public void Test_Sequential()
                 opd.Add(new Ordinate(i, i));
             // X
             lo = opd.SequentialSearchX(872.5d);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
             // Y
             lo = opd.SequentialSearchY(872.5d);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
 
         }
 
@@ -86,10 +86,10 @@ public void Test_Bisection()
                 opd.Add(new Ordinate(i, i));
             // X
             var lo = opd.BisectionSearchX(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
             // Y
             lo = opd.BisectionSearchY(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
 
             // DSC
             opd = new OrderedPairedData(true, SortOrder.Descending, false, SortOrder.Descending);
@@ -97,10 +97,10 @@ public void Test_Bisection()
                 opd.Add(new Ordinate(i, i));
             // X
             lo = opd.BisectionSearchX(872.5d);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
             // Y
             lo = opd.BisectionSearchY(872.5d);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
 
         }
 
@@ -116,10 +116,10 @@ public void Test_Hunt()
                 opd.Add(new Ordinate(i, i));
             // X
             var lo = opd.HuntSearchX(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
             // Y
             lo = opd.HuntSearchY(872.5d);
-            Assert.AreEqual(lo, 871);
+            Assert.AreEqual(871, lo);
 
             // DSC
             opd = new OrderedPairedData(true, SortOrder.Descending, false, SortOrder.Descending);
@@ -127,10 +127,10 @@ public void Test_Hunt()
                 opd.Add(new Ordinate(i, i));
             // X
             lo = opd.HuntSearchX(872.5d);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
             // Y
             lo = opd.HuntSearchY(872.5d);
-            Assert.AreEqual(lo, 127);
+            Assert.AreEqual(127, lo);
 
         }
 
@@ -147,7 +147,7 @@ public void Test_Lin()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 75d;
             double Y = opd.GetYFromX(X);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y);
@@ -168,7 +168,7 @@ public void Test_LinLog()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 75d;
             double Y = opd.GetYFromX(X, Transform.None, Transform.Logarithmic);
-            Assert.AreEqual(Y, 141.42135623731d, 1E-6);
+            Assert.AreEqual(141.42135623731d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.None, Transform.Logarithmic);
@@ -188,7 +188,7 @@ public void Test_LogLin()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 75d;
             double Y = opd.GetYFromX(X, Transform.Logarithmic, Transform.None);
-            Assert.AreEqual(Y, 158.496250072116d, 1E-6);
+            Assert.AreEqual(158.496250072116d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.Logarithmic, Transform.None);
@@ -208,7 +208,7 @@ public void Test_LogLog()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 75d;
             double Y = opd.GetYFromX(X, Transform.Logarithmic, Transform.Logarithmic);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.Logarithmic, Transform.Logarithmic);
@@ -228,7 +228,7 @@ public void Test_LinZ()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 0.18d;
             double Y = opd.GetYFromX(X, Transform.None, Transform.NormalZ);
-            Assert.AreEqual(Y, 0.358762529d, 1E-6);
+            Assert.AreEqual(0.358762529d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.None, Transform.NormalZ);
@@ -248,7 +248,7 @@ public void Test_ZLin()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 0.18d;
             double Y = opd.GetYFromX(X, Transform.NormalZ, Transform.None);
-            Assert.AreEqual(Y, 0.362146174d, 1E-6);
+            Assert.AreEqual(0.362146174d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.NormalZ, Transform.None);
@@ -268,7 +268,7 @@ public void Test_ZZ()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 0.18d;
             double Y = opd.GetYFromX(X, Transform.NormalZ, Transform.NormalZ);
-            Assert.AreEqual(Y, 0.36093855992815d, 1E-6);
+            Assert.AreEqual(0.36093855992815d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.NormalZ, Transform.NormalZ);
@@ -290,7 +290,7 @@ public void Test_RevLinear()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 75d;
             double Y = opd.GetYFromX(X);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y);
@@ -312,7 +312,7 @@ public void Test_RevLinLog()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 75d;
             double Y = opd.GetYFromX(X, Transform.None, Transform.Logarithmic);
-            Assert.AreEqual(Y, 141.42135623731d, 1E-6);
+            Assert.AreEqual(141.42135623731d, Y,  1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.None, Transform.Logarithmic);
@@ -334,7 +334,7 @@ public void Test_RevLogLin()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 75d;
             double Y = opd.GetYFromX(X, Transform.Logarithmic, Transform.None);
-            Assert.AreEqual(Y, 158.496250072116d, 1E-6);
+            Assert.AreEqual(158.496250072116d, Y, 1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.Logarithmic, Transform.None);
@@ -356,7 +356,7 @@ public void Test_RevLogLog()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 75d;
             double Y = opd.GetYFromX(X, Transform.Logarithmic, Transform.Logarithmic);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y, 1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.Logarithmic, Transform.Logarithmic);
@@ -378,7 +378,7 @@ public void Test_RevLinZ()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 0.18d;
             double Y = opd.GetYFromX(X, Transform.None, Transform.NormalZ);
-            Assert.AreEqual(Y, 0.358762529d, 1E-6);
+            Assert.AreEqual(0.358762529d, Y, 1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.None, Transform.NormalZ);
@@ -400,7 +400,7 @@ public void Test_RevZLin()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 0.18d;
             double Y = opd.GetYFromX(X, Transform.NormalZ, Transform.None);
-            Assert.AreEqual(Y, 0.362146174d, 1E-6);
+            Assert.AreEqual(0.362146174d, Y, 1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.NormalZ, Transform.None);
@@ -422,7 +422,7 @@ public void Test_RevZZ()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Descending, true, SortOrder.Descending);
             double X = 0.18d;
             double Y = opd.GetYFromX(X, Transform.NormalZ, Transform.NormalZ);
-            Assert.AreEqual(Y, 0.36093855992815d, 1E-6);
+            Assert.AreEqual(0.36093855992815d, Y, 1E-6);
 
             // Given Y
             var xFromY = opd.GetXFromY(Y, Transform.NormalZ, Transform.NormalZ);
@@ -442,7 +442,7 @@ public void Test_Lin_List()
             var opd = new OrderedPairedData(XArray, YArray, true, SortOrder.Ascending, true, SortOrder.Ascending);
             double X = 75d;
             double Y = opd.GetYFromX(X);
-            Assert.AreEqual(Y, 150.0d, 1E-6);
+            Assert.AreEqual(150.0d, Y, 1E-6);
 
             var yVals = opd.GetYFromX(XArray);
             for (int i = 1; i < YArray.Length; i++)
diff --git a/Test_Numerics/Data/Statistics/Test_BoxCox.cs b/Test_Numerics/Data/Statistics/Test_BoxCox.cs
index 6c0ce5a6..2bcfbfa8 100644
--- a/Test_Numerics/Data/Statistics/Test_BoxCox.cs
+++ b/Test_Numerics/Data/Statistics/Test_BoxCox.cs
@@ -66,7 +66,7 @@ public void Test_Fit()
             var sample = new[] { 142.25d, 141.23d, 141.33d, 140.82d, 141.31d, 140.58d, 141.58d, 142.15d, 143.07d, 142.85d, 143.17d, 142.54d, 143.07d, 142.26d, 142.97d, 143.86d, 142.57d, 142.19d, 142.35d, 142.63d, 144.15d, 144.73d, 144.7d, 144.97d, 145.12d, 144.78d, 145.06d, 143.94d, 143.77d, 144.8d, 145.67d, 145.44d, 145.56d, 145.61d, 146.05d, 145.74d, 145.83d, 143.88d, 140.39d, 139.34d, 140.05d, 137.93d, 138.78d, 139.59d, 140.54d, 141.31d, 140.42d, 140.18d, 138.43d, 138.97d, 139.31d, 139.26d, 140.08d, 141.1d, 143.48d, 143.28d, 143.5d, 143.12d, 142.14d, 142.54d, 142.24d, 142.16d, 142.97d, 143.69d, 143.67d, 144.65d, 144.33d, 144.82d, 143.74d, 144.9d, 145.83d, 146.97d, 146.6d, 146.55d, 148.22d, 148.37d, 148.23d, 148.73d, 149.49d, 149.09d, 149.64d, 148.42d, 148.9d, 149.97d, 150.75d, 150.88d, 150.58d, 150.64d, 150.73d, 149.75d, 150.86d, 150.7d, 150.8d, 151.38d, 152.01d, 152.58d, 152.7d, 152.95d, 152.53d, 151.5d, 151.94d, 151.46d, 153.67d, 153.88d, 153.54d, 153.74d, 152.86d, 151.56d, 149.58d, 150.93d, 150.67d, 150.5d, 152.06d, 153.14d, 153.38d, 152.55d, 153.58d, 151.08d, 151.52d, 150.24d, 150.21d, 148.13d, 150.38d, 150.9d, 150.87d, 152.18d, 152.4d, 152.38d, 153.16d, 152.29d, 150.75d, 152.37d, 154.57d, 154.99d, 154.93d, 154.23d, 155.2d, 154.89d, 154.18d, 153.12d, 152.02d, 150.19d, 148.21d, 145.93d, 148.33d, 145.18d, 146.76d, 147.28d, 144.21d, 145.94d, 148.41d, 147.43d, 144.39d, 146.5d, 145.7d, 142.72d, 139.79d, 145.5d, 145.17d, 144.6d, 146.01d, 147.34d, 146.48d, 147.85d, 146.16d, 144.37d, 145.45d, 147.65d, 147.45d, 148.2d, 147.95d, 146.48d, 146.52d, 146.24d, 147.29d, 148.55d, 147.96d, 148.31d, 148.83d, 153.41d, 153.34d, 152.71d, 152.42d, 150.81d, 152.25d, 152.91d, 152.85d, 152.6d, 154.61d, 153.81d, 154.11d, 155.03d, 155.39d, 155.6d, 156.04d, 156.93d, 155.46d, 156.27d, 154.41d, 154.98d };
             double l1 = 0d;
             BoxCox.FitLambda(sample, out l1);
-            Assert.AreEqual(l1, 1.670035d, 1E-4);
+            Assert.AreEqual(1.670035d, l1,  1E-4);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Data/Statistics/Test_HypothesisTests.cs b/Test_Numerics/Data/Statistics/Test_HypothesisTests.cs
index c8e90605..bd37842b 100644
--- a/Test_Numerics/Data/Statistics/Test_HypothesisTests.cs
+++ b/Test_Numerics/Data/Statistics/Test_HypothesisTests.cs
@@ -80,7 +80,7 @@ public void Test_OneSampleTtest()
             Assert.AreEqual(p, true_p, 1E-4);
 
             var t = HypothesisTests.OneSampleTtest(new double[] { 23, 15, -5, 7, 1, -10, 12, -8, 20, 8, -2, -5 });
-            Assert.AreEqual(t, 0.087585 * 2, 1E-6);
+            Assert.AreEqual(0.087585 * 2, t,  1E-6);
         }
 
         /// <summary>
@@ -187,7 +187,7 @@ public void Test_JarqueBera()
 
             // known example
             var JB = HypothesisTests.JarqueBeraTest(new double[] { 4, 5, 5, 6, 9, 12, 13, 14, 14, 19, 22, 24, 25 });
-            Assert.AreEqual(JB, 0.592128, 1E-6);
+            Assert.AreEqual(0.592128, JB, 1E-6);
         }
 
 
diff --git a/Test_Numerics/Distributions/Multivariate/Test_MultivariateNormal.cs b/Test_Numerics/Distributions/Multivariate/Test_MultivariateNormal.cs
index 98c1bb06..de4cced4 100644
--- a/Test_Numerics/Distributions/Multivariate/Test_MultivariateNormal.cs
+++ b/Test_Numerics/Distributions/Multivariate/Test_MultivariateNormal.cs
@@ -145,47 +145,47 @@ public void Test_MultivariateNormalCDF_R()
 
             // AB
             var p = mvn.CDF(new[] { Normal.StandardZ(0.25), Normal.StandardZ(0.35), double.PositiveInfinity, double.PositiveInfinity });
-            Assert.AreEqual(p, 0.05011069, 1E-4);
+            Assert.AreEqual(0.05011069, p,  1E-4);
 
             // AC
             p = mvn.CDF(new[] { Normal.StandardZ(0.25), double.PositiveInfinity, Normal.StandardZ(0.5), double.PositiveInfinity });
-            Assert.AreEqual(p, 0.0827451, 1E-4);
+            Assert.AreEqual(0.0827451, p,  1E-4);
 
             // AD
             p = mvn.CDF(new[] { Normal.StandardZ(0.25), double.PositiveInfinity, double.PositiveInfinity, Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.0827451, 1E-4);
+            Assert.AreEqual(0.0827451, p, 1E-4);
 
             // BC
             p = mvn.CDF(new[] { double.PositiveInfinity, Normal.StandardZ(0.35), Normal.StandardZ(0.5), double.PositiveInfinity });
-            Assert.AreEqual(p, 0.1254504, 1E-4);
+            Assert.AreEqual(0.1254504, p, 1E-4);
 
             // BD
             p = mvn.CDF(new[] { double.PositiveInfinity, Normal.StandardZ(0.35), double.PositiveInfinity, Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.1254504, 1E-4);
+            Assert.AreEqual(0.1254504, p, 1E-4);
 
             // CD
             p = mvn.CDF(new[] { double.PositiveInfinity, double.PositiveInfinity, Normal.StandardZ(0.5), Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.1964756, 1E-4);
+            Assert.AreEqual(0.1964756, p, 1E-4);
 
             // ABC
             p = mvn.CDF(new[] { Normal.StandardZ(0.25), Normal.StandardZ(0.35), Normal.StandardZ(0.5), double.PositiveInfinity });
-            Assert.AreEqual(p, 0.005960125, 1E-4);
+            Assert.AreEqual(0.005960125, p, 1E-4);
 
             // ABD
             p = mvn.CDF(new[] { Normal.StandardZ(0.25), Normal.StandardZ(0.35), double.PositiveInfinity, Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.005964513, 1E-4);
+            Assert.AreEqual(0.005964513, p, 1E-4);
 
             // ACD
             p = mvn.CDF(new[] { Normal.StandardZ(0.25), double.PositiveInfinity, Normal.StandardZ(0.5), Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.0128066, 1E-4);
+            Assert.AreEqual(0.0128066, p, 1E-4);
 
             // BCD
             p = mvn.CDF(new[] { double.PositiveInfinity, Normal.StandardZ(0.35), Normal.StandardZ(0.5), Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.02324389, 1E-4);
+            Assert.AreEqual(0.02324389, p, 1E-4);
 
             // ABCD
             p = mvn.CDF(new[] { Normal.StandardZ(0.25), Normal.StandardZ(0.35), Normal.StandardZ(0.5), Normal.StandardZ(0.5)});
-            Assert.AreEqual(p, 3.593582e-13, 1E-4);
+            Assert.AreEqual(3.593582e-13, p, 1E-4);
         }
 
         /// <summary>
@@ -206,7 +206,7 @@ public void Test_MultivariateNormalCDF_R_PerfectNegative()
             var mvn = new MultivariateNormal(mean, covar) { MVNUNI = new MersenneTwister(12345) };
 
             var p = mvn.CDF(new[] { Normal.StandardZ(0.5), Normal.StandardZ(0.5), Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.002740932, 1E-4);
+            Assert.AreEqual(0.002740932, p, 1E-4);
         }
 
         /// <summary>
@@ -226,7 +226,7 @@ public void Test_MultivariateNormalCDF_R_PerfectPositive()
             var mvn = new MultivariateNormal(mean, covar) { MVNUNI = new MersenneTwister(12345) };
 
             var p = mvn.CDF(new[] { Normal.StandardZ(0.5), Normal.StandardZ(0.5), Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.4661416, 1E-4);
+            Assert.AreEqual(0.4661416, p, 1E-4);
 
         }
 
@@ -246,7 +246,7 @@ public void Test_MultivariateNormalCDF_R_Independent()
             var mvn = new MultivariateNormal(mean, covar) { MVNUNI = new MersenneTwister(12345) };
 
             var p = mvn.CDF(new[] { Normal.StandardZ(0.5), Normal.StandardZ(0.5), Normal.StandardZ(0.5) });
-            Assert.AreEqual(p, 0.125, 1E-4);
+            Assert.AreEqual(0.125, p, 1E-4);
 
         }
          
diff --git a/Test_Numerics/Distributions/Univariate/Test_ChiSquared.cs b/Test_Numerics/Distributions/Univariate/Test_ChiSquared.cs
index 181b8ed8..5f626d9f 100644
--- a/Test_Numerics/Distributions/Univariate/Test_ChiSquared.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_ChiSquared.cs
@@ -276,12 +276,12 @@ public void Test_InverseCDF()
         {
             var x = new ChiSquared(1);
             Assert.AreEqual(0.09999, x.InverseCDF(0.24817036595415071751), 1e-04);
-            Assert.AreEqual(1, x.InverseCDF(0.68268949213708589717),  1e-04);
+            Assert.AreEqual(1, x.InverseCDF(0.68268949213708589717), 1e-04);
             Assert.AreEqual(5.5, x.InverseCDF(0.9809835), 1e-04);
 
             var x2 = new ChiSquared(2);
-            Assert.AreEqual(0, x2.InverseCDF(0));
-            Assert.AreEqual(0.1, x2.InverseCDF(0.04877057), 1e-04);
+            Assert.AreEqual(0,x2.InverseCDF(0));
+            Assert.AreEqual(0.1, x2.InverseCDF(0.04877057),1e-04);
             Assert.AreEqual(1, x2.InverseCDF(0.3934693),1e-04);
             Assert.AreEqual(5.5, x2.InverseCDF(0.9360721), 1e-04);
         }
diff --git a/Test_Numerics/Distributions/Univariate/Test_EmpiricalDistribution.cs b/Test_Numerics/Distributions/Univariate/Test_EmpiricalDistribution.cs
index e26b6256..4406083c 100644
--- a/Test_Numerics/Distributions/Univariate/Test_EmpiricalDistribution.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_EmpiricalDistribution.cs
@@ -270,8 +270,8 @@ public void Test_ConvolveFiveDistributions()
             Assert.AreEqual(convolved.Mean, convolved.Median, 0.5, "Median should be close to mean for symmetric distribution");
 
             // Verify CDF properties
-            Assert.IsLessThanOrEqualTo( 0.01, convolved.CDF(convolved.Minimum) );
-            Assert.IsGreaterThanOrEqualTo(0.99, convolved.CDF(convolved.Maximum));
+            Assert.IsLessThanOrEqualTo(0.01,convolved.CDF(convolved.Minimum), "CDF at minimum should be close to 0");
+            Assert.IsGreaterThanOrEqualTo(0.99,convolved.CDF(convolved.Maximum), "CDF at maximum should be close to 1");
         }
 
         /// <summary>
@@ -317,8 +317,8 @@ public void Test_ConvolveFiveDifferentDistributions()
             double meanError = Math.Abs(convolved.Mean - expectedMean) / expectedMean;
             double stdDevError = Math.Abs(convolved.StandardDeviation - expectedStdDev) / expectedStdDev;
 
-            Assert.IsLessThan(0.05, meanError);
-            Assert.IsLessThan(0.15, stdDevError);
+            Assert.IsLessThan(0.05,meanError, $"Mean error {meanError:P2} should be less than 5%");
+            Assert.IsLessThan(0.15,stdDevError, $"StdDev error {stdDevError:P2} should be less than 15%");
 
             // Verify range is reasonable
             double expectedMin = distributions.Sum(d => d.Minimum);
diff --git a/Test_Numerics/Distributions/Univariate/Test_Exponential.cs b/Test_Numerics/Distributions/Univariate/Test_Exponential.cs
index 36a32108..4e2ca62f 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Exponential.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Exponential.cs
@@ -147,7 +147,7 @@ public void Test_EXP_Quantile()
             var EXP = new Exponential(27421d, 25200d);
             double q100 = EXP.InverseCDF(0.99d);
             double true_100 = 143471d;
-            Assert.IsLessThan(0.01d, (q100 - true_100) / true_100 );
+            Assert.IsLessThan(0.01d, (q100 - true_100) / true_100);
             double p = EXP.CDF(q100);
             double true_p = 0.99d;
             Assert.AreEqual(p, true_p);
@@ -172,7 +172,7 @@ public void Test_EXP_StandardError()
             var EXP = new Exponential(27421d, 25200d);
             double se100 = Math.Sqrt(EXP.QuantileVariance(0.99d, 85, ParameterEstimationMethod.MethodOfMoments));
             double true_se100 = 15986d;
-            Assert.IsLessThan(0.01d, (se100 - true_se100) / true_se100);
+            Assert.IsLessThan(0.01d,(se100 - true_se100) / true_se100);
 
             // Maximum Likelihood
             EXP = new Exponential(12629d, 39991d);
@@ -203,11 +203,11 @@ public void Test_EXP_Partials()
         public void Test_Construction()
         {
             var EXP = new Exponential(-5, 100);
-            Assert.AreEqual(-5, EXP.Xi);
+            Assert.AreEqual(-5,EXP.Xi);
             Assert.AreEqual(100,EXP.Alpha);
 
             var EXP2 = new Exponential(0, 1);
-            Assert.AreEqual(0, EXP2.Xi);
+            Assert.AreEqual(0,EXP2.Xi);
             Assert.AreEqual(1, EXP2.Alpha);
         }
 
@@ -237,7 +237,7 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var EXP = new Exponential(1, 1);
-            Assert.AreEqual("Location (ξ)",EXP.ParametersToString[0, 0]);
+            Assert.AreEqual("Location (ξ)",EXP.ParametersToString[0, 0] );
             Assert.AreEqual("Scale (α)", EXP.ParametersToString[1, 0]);
             Assert.AreEqual("1", EXP.ParametersToString[0, 1]);
             Assert.AreEqual("1", EXP.ParametersToString[1, 1]);
@@ -349,7 +349,7 @@ public void Test_Minimum()
         public void Test_Maximum()
         {
             var EXP = new Exponential(0, 1);
-            Assert.AreEqual(double.PositiveInfinity, EXP.Maximum);
+            Assert.AreEqual(double.PositiveInfinity,EXP.Maximum);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Distributions/Univariate/Test_GammaDistribution.cs b/Test_Numerics/Distributions/Univariate/Test_GammaDistribution.cs
index 1920df3c..eb90c7a7 100644
--- a/Test_Numerics/Distributions/Univariate/Test_GammaDistribution.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_GammaDistribution.cs
@@ -75,8 +75,8 @@ public void Test_GammaDist_MOM()
             double lambda = G.Kappa;
             double trueA = 0.08317d;
             double trueL = 15.91188d;
-            Assert.IsLessThan(0.01d, (alpha - trueA) / trueA);
-            Assert.IsLessThan(0.01d, (lambda - trueL) / trueL);
+            Assert.IsLessThan(0.01d,(alpha - trueA) / trueA);
+            Assert.IsLessThan(0.01d,(lambda - trueL) / trueL);
         }
 
         [TestMethod()]
@@ -93,9 +93,9 @@ public void Test_GammaDist_LMOM_Fit()
             Assert.AreEqual(scale, true_scale, 0.0001d);
             Assert.AreEqual(shape, true_shape, 0.0001d);
             var lmom = G.LinearMomentsFromParameters(G.GetParameters);
-            Assert.AreEqual(9.9575163d, lmom[0],  0.0001d);
+            Assert.AreEqual(9.9575163d, lmom[0], 0.0001d);
             Assert.AreEqual(1.9822363d, lmom[1],  0.0001d);
-            Assert.AreEqual(0.1175059d, lmom[2],  0.0001d);
+            Assert.AreEqual(0.1175059d,lmom[2], 0.0001d);
             Assert.AreEqual(0.1268391d, lmom[3],  0.0001d);
         }
 
@@ -143,7 +143,7 @@ public void Test_GammaDist_Quantile()
             Assert.IsLessThan(0.01d, (q1000 - true_1000) / true_1000);
             double p = G.CDF(q1000);
             double true_p = 0.99d;
-            Assert.AreEqual(p,true_p);
+            Assert.AreEqual(p, true_p);
         }
 
         /// <summary>
@@ -182,7 +182,7 @@ public void Test_Construction()
         {
             var G = new GammaDistribution(2, 10);
             Assert.AreEqual(2,G.Theta);
-            Assert.AreEqual(10, G.Kappa);
+            Assert.AreEqual(10,G.Kappa);
 
             var G2 = new GammaDistribution(-1, 4);
             Assert.AreEqual(-1,G2.Theta);
@@ -197,10 +197,10 @@ public void Test_Construction()
         public void Test_Rate()
         {
             var G = new GammaDistribution(2, 2);
-            Assert.AreEqual(0.5,G.Rate);
+            Assert.AreEqual(0.5, G.Rate);
 
             var G2 = new GammaDistribution();
-            Assert.AreEqual(0.1,G2.Rate);
+            Assert.AreEqual(0.1, G2.Rate);
         }
 
         /// <summary>
@@ -210,10 +210,10 @@ public void Test_Rate()
         public void Test_ParametersToString()
         {
             var G = new GammaDistribution();
-            Assert.AreEqual("Scale (θ)", G.ParametersToString[0, 0]);
-            Assert.AreEqual("Shape (κ)",G.ParametersToString[1, 0]);
-            Assert.AreEqual("10",G.ParametersToString[0, 1]);
-            Assert.AreEqual("2",G.ParametersToString[1, 1]);
+            Assert.AreEqual("Scale (θ)",G.ParametersToString[0, 0] );
+            Assert.AreEqual("Shape (κ)", G.ParametersToString[1, 0]);
+            Assert.AreEqual("10", G.ParametersToString[0, 1]);
+            Assert.AreEqual("2", G.ParametersToString[1, 1]);
         }
 
         /// <summary>
@@ -256,7 +256,7 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var G = new GammaDistribution();
-            Assert.AreEqual(20,G.Mean);
+            Assert.AreEqual(20, G.Mean);
         }
 
         /// <summary>
@@ -302,7 +302,7 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var G = new GammaDistribution();
-            Assert.AreEqual(1.4142135, G.Skewness, 1e-04);
+            Assert.AreEqual(1.4142135, G.Skewness,  1e-04);
 
             var G2 = new GammaDistribution(10, 100);
             Assert.AreEqual(0.2, G2.Skewness);
@@ -321,7 +321,7 @@ public void Test_Kurtosis()
             Assert.AreEqual(4, G2.Kurtosis);
 
             var G3 = new GammaDistribution(10, 2.5);
-            Assert.AreEqual(5.4,G3.Kurtosis);
+            Assert.AreEqual(5.4, G3.Kurtosis);
         }
 
         /// <summary>
@@ -331,7 +331,7 @@ public void Test_Kurtosis()
         public void Test_Minimum()
         {
             var G = new GammaDistribution();
-            Assert.AreEqual(0,G.Minimum);
+            Assert.AreEqual(0, G.Minimum);
         }
 
         /// <summary>
@@ -341,7 +341,7 @@ public void Test_Minimum()
         public void Test_Maximum()
         {
             var G = new GammaDistribution();
-            Assert.AreEqual(double.PositiveInfinity,G.Maximum);
+            Assert.AreEqual(double.PositiveInfinity,G.Maximum );
         }
 
         /// <summary>
@@ -364,8 +364,8 @@ public void ValidateMLE_NR()
             double lambda = G.Kappa;
             double trueA = 0.08833d;
             double trueL = 16.89937d;
-            Assert.IsLessThan( 0.2d, (alpha - trueA) / trueA );
-            Assert.IsLessThan(0.01d, (lambda - trueL) / trueL);
+            Assert.IsLessThan(0.2d,(alpha - trueA) / trueA);
+            Assert.IsLessThan(0.01d,(lambda - trueL) / trueL);
         }
 
         /// <summary>
@@ -400,10 +400,10 @@ public void Test_PDF()
         {
             var G = new GammaDistribution(10,1);
             Assert.AreEqual(0.090483, G.PDF(1),  1e-04);
-            Assert.AreEqual(0.036787, G.PDF(10), 1e-04);
+            Assert.AreEqual(0.036787, G.PDF(10),  1e-04);
 
             var G2 = new GammaDistribution(1,1);
-            Assert.AreEqual(0.367879, G2.PDF(1), 1e-04);
+            Assert.AreEqual(0.367879, G2.PDF(1),  1e-04);
             Assert.AreEqual(0.0000453999, G2.PDF(10),  1e-10);
         }
 
@@ -418,7 +418,7 @@ public void Test_CDF()
             Assert.AreEqual(0.63212, G.CDF(10), 1e-04);
 
             var G2 = new GammaDistribution(1, 1);
-            Assert.AreEqual(0.999954, G2.CDF(10), 1e-04);
+            Assert.AreEqual(0.999954, G2.CDF(10),  1e-04);
 
             var G3 = new GammaDistribution(0.1, 10);
             Assert.AreEqual(0.54207028, G3.CDF(1), 1e-04);
diff --git a/Test_Numerics/Distributions/Univariate/Test_GeneralizedBeta.cs b/Test_Numerics/Distributions/Univariate/Test_GeneralizedBeta.cs
index b6ba675c..edb5db03 100644
--- a/Test_Numerics/Distributions/Univariate/Test_GeneralizedBeta.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_GeneralizedBeta.cs
@@ -201,10 +201,10 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var b = new GeneralizedBeta(2, 2, 0, 1);
-            Assert.AreEqual(0.5, b.Mean);
+            Assert.AreEqual(0.5,b.Mean);
 
             var b2 = new GeneralizedBeta(2, 2, -10, 10);
-            Assert.AreEqual(0, b2.Mean);
+            Assert.AreEqual(0,b2.Mean);
         }
 
         /// <summary>
@@ -224,10 +224,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var b = new GeneralizedBeta();
-            Assert.AreEqual(0.5, b.Mode);
+            Assert.AreEqual(0.5,b.Mode);
 
             var b2 = new GeneralizedBeta(2, 2, -10, 10);
-            Assert.AreEqual(0, b2.Mode);
+            Assert.AreEqual(0,b2.Mode);
         }
 
         /// <summary>
@@ -240,7 +240,7 @@ public void Test_StandardDeviation()
             Assert.AreEqual(0.223606, b.StandardDeviation,  1e-04);
 
             var b2 = new GeneralizedBeta(2, 2, -10, 10);
-            Assert.AreEqual(4.47213, b2.StandardDeviation, 1e-04);
+            Assert.AreEqual(4.47213, b2.StandardDeviation,1e-04);
         }
 
         /// <summary>
@@ -250,7 +250,7 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var b = new GeneralizedBeta();
-            Assert.AreEqual(0, b.Skewness);
+            Assert.AreEqual(0,b.Skewness);
 
             var b2 = new GeneralizedBeta(2, 10);
             Assert.AreEqual(0.92140088, b2.Skewness, 1e-04);
@@ -279,12 +279,12 @@ public void Test_Kurtosis()
         public void Test_MinimumMaximum()
         {
             var b = new GeneralizedBeta();
-            Assert.AreEqual(0, b.Minimum);
-            Assert.AreEqual(1, b.Maximum);
+            Assert.AreEqual(0,b.Minimum);
+            Assert.AreEqual(1,b.Maximum);
 
             var b2 = new GeneralizedBeta(2, 2, -10, 10);
-            Assert.AreEqual(-10, b2.Minimum);
-            Assert.AreEqual(10, b2.Maximum);
+            Assert.AreEqual(-10,b2.Minimum);
+            Assert.AreEqual(10,b2.Maximum);
         }
 
         /// <summary>
@@ -314,7 +314,7 @@ public void Test_CDF()
         {
             var b = new GeneralizedBeta(2,2,-10,10);
             Assert.AreEqual(0,b.CDF(-11));
-            Assert.AreEqual(1, b.CDF(11));
+            Assert.AreEqual(1,b.CDF(11));
 
             var b2 = new GeneralizedBeta(9, 1);
             Assert.AreEqual(0, b2.CDF(0));
diff --git a/Test_Numerics/Distributions/Univariate/Test_GeneralizedExtremeValue.cs b/Test_Numerics/Distributions/Univariate/Test_GeneralizedExtremeValue.cs
index 2632de48..ca9f0ce3 100644
--- a/Test_Numerics/Distributions/Univariate/Test_GeneralizedExtremeValue.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_GeneralizedExtremeValue.cs
@@ -205,7 +205,7 @@ public void Test_GEV_StandardError()
             var covar = GEV.ParameterCovariance(sample.Length, ParameterEstimationMethod.MaximumLikelihood);
             double qVar = GEV.QuantileVariance(0.99d, sample.Length, ParameterEstimationMethod.MaximumLikelihood);
             double qSigma = Math.Sqrt(qVar);
-            Assert.IsLessThan( 0.01d, (partials[0] - true_dXdU) / true_dXdU );
+            Assert.IsLessThan(0.01d,(partials[0] - true_dXdU) / true_dXdU);
             Assert.IsLessThan(0.01d, (partials[1] - true_dxdA) / true_dxdA);
             Assert.IsLessThan(0.01d, (partials[2] - true_dxdK) / true_dxdK);
             Assert.IsLessThan(0.01d, (covar[0, 0] - true_VarU) / true_VarU);
@@ -333,7 +333,7 @@ public void Test_StandardDeviation()
             Assert.AreEqual(12.825498, GEV.StandardDeviation,  1e-05);
 
             var GEV2 = new GeneralizedExtremeValue(100, 10, 0.49);
-            Assert.AreEqual(9.280898, GEV2.StandardDeviation,  1e-04);
+            Assert.AreEqual(9.280898, GEV2.StandardDeviation, 1e-04);
 
             var GEV3 = new GeneralizedExtremeValue(100, 10, 1);
             Assert.AreEqual(double.NaN, GEV3.StandardDeviation);
@@ -346,13 +346,13 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var GEV = new GeneralizedExtremeValue();
-            Assert.AreEqual(1.1396,GEV.Skewness );
+            Assert.AreEqual(1.1396, GEV.Skewness);
 
             var GEV2 = new GeneralizedExtremeValue(100, 10, 0.3);
             Assert.AreEqual(-0.0690175, GEV2.Skewness,  1e-03);
 
             var GEV3 = new GeneralizedExtremeValue(100, 10, 1);
-            Assert.AreEqual(double.NaN,GEV3.Skewness);
+            Assert.AreEqual(double.NaN, GEV3.Skewness);
         }
 
         /// <summary>
@@ -378,7 +378,7 @@ public void Test_Kurtosis()
         public void Test_Minimum()
         {
             var GEV = new GeneralizedExtremeValue();
-            Assert.AreEqual(double.NegativeInfinity,GEV.Minimum);
+            Assert.AreEqual(double.NegativeInfinity, GEV.Minimum);
 
             var GEV2 = new GeneralizedExtremeValue(100, 10, -5);
             Assert.AreEqual(98, GEV2.Minimum);
diff --git a/Test_Numerics/Distributions/Univariate/Test_GeneralizedLogistic.cs b/Test_Numerics/Distributions/Univariate/Test_GeneralizedLogistic.cs
index 224c58d0..d5560ded 100644
--- a/Test_Numerics/Distributions/Univariate/Test_GeneralizedLogistic.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_GeneralizedLogistic.cs
@@ -117,9 +117,9 @@ public void Test_GLO_LMOM_Fit()
             Assert.AreEqual(k, true_k, 0.001d);
             var lmom = GLO.LinearMomentsFromParameters(GLO.GetParameters);
             Assert.AreEqual(1648.806d, lmom[0],  0.001d);
-            Assert.AreEqual(138.2366d, lmom[1],  0.001d);
-            Assert.AreEqual(0.1033903d, lmom[2],  0.001d);
-            Assert.AreEqual(0.1755746d, lmom[3],  0.001d);
+            Assert.AreEqual(138.2366d, lmom[1], 0.001d);
+            Assert.AreEqual(0.1033903d, lmom[2], 0.001d);
+            Assert.AreEqual(0.1755746d, lmom[3], 0.001d);
         }
 
         /// <summary>
@@ -169,7 +169,7 @@ public void Test_GLO_Quantile()
             Assert.IsLessThan(0.01d, (q100 - true_100) / true_100);
             double p = GLO.CDF(q100);
             double true_p = 0.99d;
-            Assert.AreEqual(p,true_p);
+            Assert.AreEqual(p, true_p);
         }
 
         /// <summary>
@@ -199,7 +199,7 @@ public void Test_Construction()
             var l = new GeneralizedLogistic();
             Assert.AreEqual(100,l.Xi);
             Assert.AreEqual(10,l.Alpha);
-            Assert.AreEqual(0, l.Kappa);
+            Assert.AreEqual(0,l.Kappa);
 
             var l2 = new GeneralizedLogistic(-100, 10, 1);
             Assert.AreEqual(-100,l2.Xi);
@@ -323,7 +323,7 @@ public void Test_Skewness()
             Assert.AreEqual(-10.90354, l2.Skewness,  1e-04);
 
             var l3 = new GeneralizedLogistic(100, 10, 1);
-            Assert.AreEqual(double.NaN,l3.Skewness);
+            Assert.AreEqual(double.NaN, l3.Skewness);
         }
 
         /// <summary>
@@ -404,7 +404,7 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var l = new GeneralizedLogistic();
-            Assert.AreEqual(double.NegativeInfinity,l.InverseCDF(0) );
+            Assert.AreEqual(double.NegativeInfinity,l.InverseCDF(0));
             Assert.AreEqual(100, l.InverseCDF(0.5));
             Assert.AreEqual(double.PositiveInfinity, l.InverseCDF(1));
 
diff --git a/Test_Numerics/Distributions/Univariate/Test_GeneralizedPareto.cs b/Test_Numerics/Distributions/Univariate/Test_GeneralizedPareto.cs
index 53d8227b..501e3edc 100644
--- a/Test_Numerics/Distributions/Univariate/Test_GeneralizedPareto.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_GeneralizedPareto.cs
@@ -110,8 +110,8 @@ public void Test_GPA_LMOM_Fit()
             var lmom = GPA.LinearMomentsFromParameters(GPA.GetParameters);
             Assert.AreEqual(1648.806d, lmom[0],  0.001d);
             Assert.AreEqual(138.2366d, lmom[1],  0.001d);
-            Assert.AreEqual(0.1033903d, lmom[2],  0.001d);
-            Assert.AreEqual(0.03073215d, lmom[3],  0.001d);
+            Assert.AreEqual(0.1033903d, lmom[2], 0.001d);
+            Assert.AreEqual(0.03073215d, lmom[3], 0.001d);
         }
 
         /// <summary>
@@ -312,7 +312,7 @@ public void Test_Mean()
             Assert.AreEqual(105.26315, GPA2.Mean,  1e-04);
 
             var GPA3 = new GeneralizedPareto(100, 10, 1);
-            Assert.AreEqual(double.NaN,GPA3.Mean );
+            Assert.AreEqual(double.NaN,GPA3.Mean);
         }
 
         /// <summary>
@@ -335,7 +335,7 @@ public void Test_Median()
         public void Test_Mode()
         {
             var GPA = new GeneralizedPareto();
-            Assert.AreEqual(100,GPA.Mode );
+            Assert.AreEqual(100, GPA.Mode);
 
             var GPA2 = new GeneralizedPareto(100, 10, 1);
             Assert.AreEqual(95, GPA2.Mode);
@@ -396,7 +396,7 @@ public void Test_Kurtosis()
         public void Test_Minimum()
         {
             var GPA = new GeneralizedPareto();
-            Assert.AreEqual(100,GPA.Minimum);
+            Assert.AreEqual(100, GPA.Minimum);
         }
 
         /// <summary>
@@ -435,7 +435,7 @@ public void Test_CDF()
         {
             var GPA = new GeneralizedPareto();
             Assert.AreEqual(0, GPA.CDF(100));
-            Assert.AreEqual(0, GPA.CDF(0),  1e-04);
+            Assert.AreEqual(0, GPA.CDF(0), 1e-04);
             Assert.AreEqual(0.999954, GPA.CDF(200),  1e-06);
 
             var GPA2 = new GeneralizedPareto(100, 10, 1);
@@ -451,8 +451,8 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var GPA = new GeneralizedPareto();
-            Assert.AreEqual(100,GPA.InverseCDF(0));
-            Assert.AreEqual(double.PositiveInfinity, GPA.InverseCDF(1));
+            Assert.AreEqual(100, GPA.InverseCDF(0));
+            Assert.AreEqual(double.PositiveInfinity,GPA.InverseCDF(1));
             Assert.AreEqual(106.93147, GPA.InverseCDF(0.5),  1e-04);
 
             var GPA2 = new GeneralizedPareto(100, 10, 1);
diff --git a/Test_Numerics/Distributions/Univariate/Test_Geometric.cs b/Test_Numerics/Distributions/Univariate/Test_Geometric.cs
index 26fbdae3..fe9293c7 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Geometric.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Geometric.cs
@@ -91,7 +91,7 @@ public void Test_GeometricDist()
         public void Test_Construction()
         {
             var G = new Geometric();
-            Assert.AreEqual(0.5,G.ProbabilityOfSuccess);
+            Assert.AreEqual(0.5, G.ProbabilityOfSuccess);
 
             var G2 = new Geometric(0);
             Assert.AreEqual(0, G2.ProbabilityOfSuccess);
@@ -140,7 +140,7 @@ public void Test_Mean()
             Assert.AreEqual(1, G.Mean);
 
             var G2 = new Geometric(0.3);
-            Assert.AreEqual(2.3333, G2.Mean,  1e-04);
+            Assert.AreEqual(2.3333, G2.Mean, 1e-04);
         }
 
         /// <summary>
@@ -239,7 +239,7 @@ public void Test_PDF()
 
             var G2 = new Geometric(0.3);
             Assert.AreEqual(0.3, G2.PDF(0));
-            Assert.AreEqual(0.122989, G2.PDF(2.5),  1e-05);
+            Assert.AreEqual(0.122989, G2.PDF(2.5), 1e-05);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Distributions/Univariate/Test_Gumbel.cs b/Test_Numerics/Distributions/Univariate/Test_Gumbel.cs
index 67dfb423..0c0ff506 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Gumbel.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Gumbel.cs
@@ -188,7 +188,7 @@ public void Test_Construction()
 
             var GUM2 = new Gumbel(-100, 1);
             Assert.AreEqual(-100,GUM2.Xi);
-            Assert.AreEqual(1,GUM2.Alpha);
+            Assert.AreEqual(1, GUM2.Alpha);
         }
 
         /// <summary>
@@ -257,7 +257,7 @@ public void Test_Median()
             Assert.AreEqual(103.66512, GUM.Median,  1e-05);
 
             var GUM2 = new Gumbel(10, 1);
-            Assert.AreEqual(10.366512, GUM2.Median,  1e-04);
+            Assert.AreEqual(10.366512, GUM2.Median, 1e-04);
         }
 
         /// <summary>
@@ -270,7 +270,7 @@ public void Test_StandardDeviation()
             Assert.AreEqual(12.82549, GUM.StandardDeviation,  1e-04);
 
             var GUM2 = new Gumbel(10, 1);
-            Assert.AreEqual(1.28254, GUM2.StandardDeviation,  1e-04);
+            Assert.AreEqual(1.28254, GUM2.StandardDeviation, 1e-04);
         }
 
         /// <summary>
@@ -336,7 +336,7 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var GUM = new Gumbel();
-            Assert.AreEqual(0.36787, GUM.CDF(100),  1e-04);
+            Assert.AreEqual(0.36787, GUM.CDF(100), 1e-04);
             Assert.AreEqual(3.5073e-65, GUM.CDF(50),  1e-68);
             Assert.AreEqual(0,GUM.CDF(-10));
 
diff --git a/Test_Numerics/Distributions/Univariate/Test_InverseChiSquared.cs b/Test_Numerics/Distributions/Univariate/Test_InverseChiSquared.cs
index 73f86cf1..ee0651a9 100644
--- a/Test_Numerics/Distributions/Univariate/Test_InverseChiSquared.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_InverseChiSquared.cs
@@ -216,7 +216,7 @@ public void Test_MinMax()
         public void Test_PDF()
         {
             var IX = new InverseChiSquared(1, 1);
-            Assert.AreEqual(0.2419, IX.PDF(1),1e-04);
+            Assert.AreEqual(0.2419, IX.PDF(1), 1e-04);
 
             var IX2 = new InverseChiSquared(2, 1);
             Assert.AreEqual(0.15163, IX2.PDF(2),  1e-04);
diff --git a/Test_Numerics/Distributions/Univariate/Test_InverseGamma.cs b/Test_Numerics/Distributions/Univariate/Test_InverseGamma.cs
index 47130b7b..eb43498b 100644
--- a/Test_Numerics/Distributions/Univariate/Test_InverseGamma.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_InverseGamma.cs
@@ -83,7 +83,7 @@ public void Test_Construction()
 
             var IG2 = new InverseGamma(2, 4);
             Assert.AreEqual(2,IG2.Beta);
-            Assert.AreEqual(4,IG2.Alpha);
+            Assert.AreEqual(4, IG2.Alpha);
         }
 
         /// <summary>
@@ -109,10 +109,10 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var IG = new InverseGamma();
-            Assert.AreEqual("Scale (β)",IG.ParametersToString[0, 0]);
-            Assert.AreEqual("Shape (α)",IG.ParametersToString[1, 0] );
-            Assert.AreEqual("0.5",IG.ParametersToString[0, 1] );
-            Assert.AreEqual("2",IG.ParametersToString[1, 1]);
+            Assert.AreEqual("Scale (β)",IG.ParametersToString[0, 0] );
+            Assert.AreEqual("Shape (α)", IG.ParametersToString[1, 0]);
+            Assert.AreEqual("0.5", IG.ParametersToString[0, 1]);
+            Assert.AreEqual("2", IG.ParametersToString[1, 1]);
         }
 
         /// <summary>
@@ -122,7 +122,7 @@ public void Test_ParametersToString()
         public void Test_Mean()
         {
             var IG = new InverseGamma();
-            Assert.AreEqual(0.5,IG.Mean);
+            Assert.AreEqual(0.5, IG.Mean);
 
             var IG2 = new InverseGamma(1, 1);
             Assert.AreEqual(double.NaN,IG2.Mean);
@@ -148,7 +148,7 @@ public void Test_Mode()
             Assert.AreEqual(0.1666, IG.Mode,  1e-04);
 
             var IG2 = new InverseGamma(1, 1);
-            Assert.AreEqual(0.5,IG2.Mode);
+            Assert.AreEqual(0.5, IG2.Mode);
         }
 
         /// <summary>
@@ -243,7 +243,7 @@ public void Test_InverseCDF()
         {
             var IG = new InverseGamma();
             Assert.AreEqual(0, IG.InverseCDF(0));
-            Assert.AreEqual(double.PositiveInfinity,IG.InverseCDF(1));
+            Assert.AreEqual(double.PositiveInfinity, IG.InverseCDF(1));
 
             var IG2 = new InverseGamma(2, 2);
             Assert.AreEqual(0.81993, IG2.InverseCDF(0.3), 1e-04);
diff --git a/Test_Numerics/Distributions/Univariate/Test_LnNormal.cs b/Test_Numerics/Distributions/Univariate/Test_LnNormal.cs
index dd7d31ec..30177e06 100644
--- a/Test_Numerics/Distributions/Univariate/Test_LnNormal.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_LnNormal.cs
@@ -182,7 +182,7 @@ public void Test_Construction()
             Assert.AreEqual(10, LN.StandardDeviation,  1E-4);
 
             var LN2 = new LnNormal(1, 1);
-            Assert.AreEqual(1, LN2.Mean,  1E-4);
+            Assert.AreEqual(1, LN2.Mean, 1E-4);
             Assert.AreEqual(1, LN2.StandardDeviation, 1E-4);
         }
 
diff --git a/Test_Numerics/Distributions/Univariate/Test_LogNormal.cs b/Test_Numerics/Distributions/Univariate/Test_LogNormal.cs
index 36baac9d..39e11410 100644
--- a/Test_Numerics/Distributions/Univariate/Test_LogNormal.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_LogNormal.cs
@@ -80,8 +80,8 @@ public void Test_LogNormal_MOM_Fit()
             double u2 = LogN.Sigma;
             double true_u1 = 10.716952223744224d;
             double true_u2 = 0.45007398831588075d;
-            Assert.IsLessThan( 0.01d, (u1 - true_u1) / true_u1 );
-            Assert.IsLessThan(0.01d, (u2 - true_u2) / true_u2 );
+            Assert.IsLessThan(0.01d, (u1 - true_u1) / true_u1);
+            Assert.IsLessThan(0.01d, (u2 - true_u2) / true_u2);
         }
 
         /// <summary>
@@ -102,7 +102,7 @@ public void Test_LogNormal_LMOM_Fit()
             Assert.AreEqual(u2, true_u2, 0.0001d);
             var lmom = norm.LinearMomentsFromParameters(norm.GetParameters);
             Assert.AreEqual(0.96723909d, lmom[0],  0.0001d);
-            Assert.AreEqual(0.09452119d, lmom[1],  0.0001d);
+            Assert.AreEqual(0.09452119d, lmom[1], 0.0001d);
             Assert.AreEqual(0.00000000d, lmom[2], 0.0001d);
             Assert.AreEqual(0.12260172d, lmom[3], 0.0001d);
         }
@@ -261,7 +261,7 @@ public void Test_PDF()
             Assert.AreEqual(3.32e-135, LogN.PDF(0.1), 1e-04);
 
             var LogN2 = new LogNormal(-0.1, 0.1);
-            Assert.AreEqual(9.12888e-56, LogN.PDF(0.8),  1e-04);
+            Assert.AreEqual(9.12888e-56, LogN.PDF(0.8), 1e-04);
         }
 
         /// <summary>
@@ -274,7 +274,7 @@ public void Test_CDF()
             Assert.AreEqual(0, LogN.CDF(0.1));
 
             var LogN2 = new LogNormal(1.5, 1.5);
-            Assert.AreEqual(0.11493, LogN2.CDF(0.5),  1e-05);
+            Assert.AreEqual(0.11493, LogN2.CDF(0.5), 1e-05);
         }
 
         /// <summary>
@@ -284,7 +284,7 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var LogN = new LogNormal(2.5, 2.5);
-            Assert.AreEqual(40183.99248, LogN.InverseCDF(0.8),  1e-04);
+            Assert.AreEqual(40183.99248, LogN.InverseCDF(0.8), 1e-04);
 
             var LogN2 = new LogNormal(1.5, 2.5);
             Assert.AreEqual(40183.99248, LogN.InverseCDF(0.8), 1e-05);
diff --git a/Test_Numerics/Distributions/Univariate/Test_LogPearsonTypeIII.cs b/Test_Numerics/Distributions/Univariate/Test_LogPearsonTypeIII.cs
index 8e6f4fac..fd84945e 100644
--- a/Test_Numerics/Distributions/Univariate/Test_LogPearsonTypeIII.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_LogPearsonTypeIII.cs
@@ -198,7 +198,7 @@ public void Test_LP3_StandardError()
             LP3 = new LogPearsonTypeIII(2.26878d, 0.10621d, -0.02925d);
             qVar999 = Math.Sqrt(LP3.QuantileVariance(0.99d, 69, ParameterEstimationMethod.MaximumLikelihood));
             true_qVar999 = 25d;
-            Assert.IsLessThan(0.01d, (qVar999 - true_qVar999) / true_qVar999 );
+            Assert.IsLessThan(0.01d, (qVar999 - true_qVar999) / true_qVar999);
 
         }
 
@@ -286,13 +286,13 @@ public void Test_Mode()
         public void Test_Minimum()
         {
             var LP3 = new LogPearsonTypeIII();
-            Assert.AreEqual(0, LP3.Minimum );
+            Assert.AreEqual(0, LP3.Minimum);
 
             var LP3ii = new LogPearsonTypeIII(1,1,1);
             Assert.AreEqual(0.1, LP3ii.Minimum,  1e-05);
 
             var LP3iii = new LogPearsonTypeIII(1, -1, 1);
-            Assert.AreEqual(0,LP3iii.Minimum);
+            Assert.AreEqual(0, LP3iii.Minimum);
         }
 
         /// <summary>
@@ -329,7 +329,7 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var LP3 = new LogPearsonTypeIII();
-            Assert.AreEqual(0,LP3.CDF(-1));
+            Assert.AreEqual(0, LP3.CDF(-1));
             Assert.AreEqual(9.8658e-10, LP3.CDF(1), 1e-13);
         }
 
diff --git a/Test_Numerics/Distributions/Univariate/Test_NoncentralT.cs b/Test_Numerics/Distributions/Univariate/Test_NoncentralT.cs
index 4cda1a3d..f8e17c82 100644
--- a/Test_Numerics/Distributions/Univariate/Test_NoncentralT.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_NoncentralT.cs
@@ -161,7 +161,7 @@ public void Test_Mean()
             Assert.AreEqual(0, t.Mean);
 
             var t2 = new NoncentralT(0, 1);
-            Assert.AreEqual(double.NaN,t2.Mean);
+            Assert.AreEqual(double.NaN, t2.Mean);
         }
 
         /// <summary>
@@ -187,7 +187,7 @@ public void Test_Mode()
             Assert.AreEqual(0, t.Mode,  1E-4);
 
             var t3 = new NoncentralT(10, 1);
-            Assert.AreEqual(0.9329, t3.Mode,  1e-04);
+            Assert.AreEqual(0.9329, t3.Mode, 1e-04);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Distributions/Univariate/Test_Normal.cs b/Test_Numerics/Distributions/Univariate/Test_Normal.cs
index a4984e42..889f4e54 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Normal.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Normal.cs
@@ -105,9 +105,9 @@ public void Test_Normal_LMOM_Fit()
             Assert.AreEqual(u2, true_u2, 0.0001d);
             var lmom = norm.LinearMomentsFromParameters(norm.GetParameters);
             Assert.AreEqual(9.9575163d, lmom[0],  0.0001d);
-            Assert.AreEqual(1.9822411d, lmom[1],  0.0001d);
+            Assert.AreEqual(1.9822411d, lmom[1], 0.0001d);
             Assert.AreEqual(0.0000000d, lmom[2], 0.0001d);
-            Assert.AreEqual(0.1226017d, lmom[3],  0.0001d);
+            Assert.AreEqual(0.1226017d, lmom[3], 0.0001d);
         }
 
         /// <summary>
@@ -219,9 +219,9 @@ public void Test_ParametersToString()
         {
             var N = new Normal();
             Assert.AreEqual("Mean (µ)",N.ParametersToString[0, 0]);
-            Assert.AreEqual("Std Dev (σ)",N.ParametersToString[1, 0]);
-            Assert.AreEqual("0",N.ParametersToString[0, 1] );
-            Assert.AreEqual("1",N.ParametersToString[1,1]);
+            Assert.AreEqual("Std Dev (σ)",N.ParametersToString[1, 0] );
+            Assert.AreEqual("0",N.ParametersToString[0, 1]);
+            Assert.AreEqual("1",N.ParametersToString[1,1] );
         }
 
         /// <summary>
@@ -339,10 +339,10 @@ public void Test_PDF()
         {
             var N = new Normal();
             Assert.AreEqual(0.39894, N.PDF(0),  1e-04);
-            Assert.AreEqual(0.24197, N.PDF(1),  1e-04);
+            Assert.AreEqual(0.24197, N.PDF(1), 1e-04);
 
             var N2 = new Normal(5, 9);
-            Assert.AreEqual(0.03549, N2.PDF(-1),  1e-04);
+            Assert.AreEqual(0.03549, N2.PDF(-1), 1e-04);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Distributions/Univariate/Test_Pareto.cs b/Test_Numerics/Distributions/Univariate/Test_Pareto.cs
index 564b719c..b160758a 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Pareto.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Pareto.cs
@@ -250,7 +250,7 @@ public void Test_PDF()
             Assert.AreEqual(4d / 9d,p.PDF(1.5) );
 
             var p2 = new Pareto(3, 2);
-            Assert.AreEqual(2d / 3d,p2.PDF(3) );
+            Assert.AreEqual(2d / 3d,p2.PDF(3));
             Assert.AreEqual(18d / 125d,p2.PDF(5) );
         }
 
@@ -273,7 +273,7 @@ public void Test_InverseCDF()
         {
             var p = new Pareto();
             Assert.AreEqual(1, p.InverseCDF(0));
-            Assert.AreEqual(1.0363, p.InverseCDF(0.3),  1e-04);
+            Assert.AreEqual(1.0363, p.InverseCDF(0.3), 1e-04);
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_PearsonTypeIII.cs b/Test_Numerics/Distributions/Univariate/Test_PearsonTypeIII.cs
index 345cd879..45606a90 100644
--- a/Test_Numerics/Distributions/Univariate/Test_PearsonTypeIII.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_PearsonTypeIII.cs
@@ -125,9 +125,9 @@ public void Test_P3_LMOM_Fit()
             Assert.AreEqual(b, true_b, 0.001d);
             var lmom = P3.LinearMomentsFromParameters(P3.GetParameters);
             Assert.AreEqual(1648.806d, lmom[0],  0.001d);
-            Assert.AreEqual(138.2366d, lmom[1],  0.001d);
-            Assert.AreEqual(0.1033889d, lmom[2],  0.001d);
-            Assert.AreEqual(0.1258521d, lmom[3],  0.001d);
+            Assert.AreEqual(138.2366d, lmom[1], 0.001d);
+            Assert.AreEqual(0.1033889d, lmom[2], 0.001d);
+            Assert.AreEqual(0.1258521d, lmom[3], 0.001d);
         }
 
         /// <summary>
@@ -220,7 +220,7 @@ public void Test_P3_StandardError()
             P3 = new PearsonTypeIII(191.31739d, 47.01925d, 0.61897d);
             qVar999 = Math.Sqrt(P3.QuantileVariance(0.99d, 69, ParameterEstimationMethod.MaximumLikelihood));
             true_qVar999 = 20.045d;
-            Assert.IsLessThan(0.01d, (qVar999 - true_qVar999) / true_qVar999 );
+            Assert.IsLessThan(0.01d, (qVar999 - true_qVar999) / true_qVar999);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Distributions/Univariate/Test_Pert.cs b/Test_Numerics/Distributions/Univariate/Test_Pert.cs
index ab69bf80..bbc4ea78 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Pert.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Pert.cs
@@ -268,7 +268,7 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var p = new Pert();
-            Assert.AreEqual(2.3333, p.Kurtosis,1e-04);
+            Assert.AreEqual(2.3333, p.Kurtosis, 1e-04);
         }
 
         /// <summary>
diff --git a/Test_Numerics/Distributions/Univariate/Test_Poisson.cs b/Test_Numerics/Distributions/Univariate/Test_Poisson.cs
index 61f092f3..0f7694b3 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Poisson.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Poisson.cs
@@ -159,7 +159,7 @@ public void Test_Mean()
         public void Test_Median()
         {
             var P = new Poisson();
-            Assert.AreEqual(1, P.Median,  1E-4);
+            Assert.AreEqual(1, P.Median, 1E-4);
         }
 
         /// <summary>
@@ -225,7 +225,7 @@ public void Test_MinMax()
             Assert.AreEqual(double.PositiveInfinity,P.Maximum);
 
             var P2 = new Poisson(4);
-            Assert.AreEqual(0, P2.Minimum   );
+            Assert.AreEqual(0, P2.Minimum);
             Assert.AreEqual(double.PositiveInfinity, P2.Maximum);
         }
 
@@ -252,7 +252,7 @@ public void Test_CDF()
         {
             var P = new Poisson(1.5);
             Assert.AreEqual(0.55782, P.CDF(1),  1e-04);
-            Assert.AreEqual(0.999999, P.CDF(10),  1e-06);
+            Assert.AreEqual(0.999999, P.CDF(10), 1e-06);
 
             var P2 = new Poisson(10.8);
             Assert.AreEqual(0.00024, P2.CDF(1),  1e-05);
diff --git a/Test_Numerics/Distributions/Univariate/Test_Rayleigh.cs b/Test_Numerics/Distributions/Univariate/Test_Rayleigh.cs
index dce618fa..66b5c9c6 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Rayleigh.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Rayleigh.cs
@@ -84,10 +84,10 @@ public void Test_RayleighDist()
         public void Test_Construction()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Sigma, 10);
+            Assert.AreEqual(10,R.Sigma);
 
             var R2 = new Rayleigh(2);
-            Assert.AreEqual(R2.Sigma, 2);
+            Assert.AreEqual(2, R2.Sigma);
         }
 
         /// <summary>
@@ -113,8 +113,8 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.ParametersToString[0, 0], "Scale (σ)");
-            Assert.AreEqual(R.ParametersToString[0, 1], "10");
+            Assert.AreEqual("Scale (σ)", R.ParametersToString[0, 0]);
+            Assert.AreEqual("10", R.ParametersToString[0, 1]);
         }
 
         /// <summary>
@@ -138,10 +138,10 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Mean, 12.53314, 1e-04);
+            Assert.AreEqual(12.53314, R.Mean,  1e-04);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R2.Mean, 1.25331, 1e-04);
+            Assert.AreEqual(1.25331, R2.Mean,  1e-04);
         }
 
         /// <summary>
@@ -151,10 +151,10 @@ public void Test_Mean()
         public void Test_Median()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Median, 11.7741, 1e-04);
+            Assert.AreEqual(11.7741, R.Median,  1e-04);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R2.Median, 1.1774, 1e-04);
+            Assert.AreEqual(1.1774, R2.Median, 1e-04);
         }
 
         /// <summary>
@@ -164,10 +164,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Mode, 10);
+            Assert.AreEqual(10, R.Mode);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R2.Mode, 1);
+            Assert.AreEqual(1, R2.Mode);
         }
         
         /// <summary>
@@ -177,10 +177,10 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.StandardDeviation, 6.55136, 1e-05);
+            Assert.AreEqual(6.55136, R.StandardDeviation,  1e-05);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R2.StandardDeviation, 0.65513, 1e-04);
+            Assert.AreEqual(0.65513, R2.StandardDeviation, 1e-04);
         }
 
         /// <summary>
@@ -190,10 +190,10 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Skewness, 0.63111, 1e-04);
+            Assert.AreEqual(0.63111, R.Skewness,  1e-04);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R2.Skewness, 0.63111, 1e-04);
+            Assert.AreEqual(0.63111, R2.Skewness,  1e-04);
         }
 
         /// <summary>
@@ -203,10 +203,10 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Kurtosis, 3.24508,1e-05);
+            Assert.AreEqual(3.24508, R.Kurtosis, 1e-05);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R2.Kurtosis, 3.24508,1e-05);
+            Assert.AreEqual(3.24508, R2.Kurtosis,1e-05);
         }
 
         /// <summary>
@@ -216,8 +216,8 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.Minimum, 0);
-            Assert.AreEqual(R.Maximum, double.PositiveInfinity);
+            Assert.AreEqual(0,R.Minimum);
+            Assert.AreEqual(double.PositiveInfinity,R.Maximum);
         }
 
         /// <summary>
@@ -227,11 +227,11 @@ public void Test_MinMax()
         public void Test_PDF()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.PDF(-1), 0);
-            Assert.AreEqual(R.PDF(1), 9.9501e-03, 1e-06);
+            Assert.AreEqual(0,R.PDF(-1));
+            Assert.AreEqual(9.9501e-03, R.PDF(1),  1e-06);
 
             var R2 = new Rayleigh(1);
-            Assert.AreEqual(R.PDF(2), 0.019603, 1e-05);
+            Assert.AreEqual(0.019603, R.PDF(2),  1e-05);
         }
 
         /// <summary>
@@ -241,8 +241,8 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.CDF(-1), 0);
-            Assert.AreEqual(R.CDF(1), 4.9875e-03,1e-04);
+            Assert.AreEqual(0, R.CDF(-1));
+            Assert.AreEqual(4.9875e-03, R.CDF(1),1e-04);
         }
 
         /// <summary>
@@ -252,9 +252,9 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var R = new Rayleigh();
-            Assert.AreEqual(R.InverseCDF(0), 0);
-            Assert.AreEqual(R.InverseCDF(1), double.PositiveInfinity);
-            Assert.AreEqual(R.InverseCDF(0.4), 10.1076, 1e-04);
+            Assert.AreEqual(0, R.InverseCDF(0));
+            Assert.AreEqual(double.PositiveInfinity,R.InverseCDF(1) );
+            Assert.AreEqual(10.1076, R.InverseCDF(0.4),  1e-04);
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_StudentT.cs b/Test_Numerics/Distributions/Univariate/Test_StudentT.cs
index eddf7d04..e8e66234 100644
--- a/Test_Numerics/Distributions/Univariate/Test_StudentT.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_StudentT.cs
@@ -111,14 +111,14 @@ public void Test_StudentT_InverseCDF()
         public void Test_Construction()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Mu, 0);
-            Assert.AreEqual(t.Sigma, 1);
-            Assert.AreEqual(t.DegreesOfFreedom, 10);
+            Assert.AreEqual(0, t.Mu);
+            Assert.AreEqual(1, t.Sigma);
+            Assert.AreEqual(10, t.DegreesOfFreedom);
 
             var t2 = new StudentT(10, 10, 10);
-            Assert.AreEqual(t2.Mu, 10);
-            Assert.AreEqual(t2.Sigma, 10);
-            Assert.AreEqual(t2.DegreesOfFreedom, 10);
+            Assert.AreEqual(10, t2.Mu);
+            Assert.AreEqual(10, t2.Sigma);
+            Assert.AreEqual(10, t2.DegreesOfFreedom);
         }
 
         /// <summary>
@@ -144,12 +144,12 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.ParametersToString[0, 0], "Location (µ)");
-            Assert.AreEqual(t.ParametersToString[1, 0], "Scale (σ)");
-            Assert.AreEqual(t.ParametersToString[2, 0], "Degrees of Freedom (ν)");
-            Assert.AreEqual(t.ParametersToString[0, 1], "0");
-            Assert.AreEqual(t.ParametersToString[1,1],"1");
-            Assert.AreEqual(t.ParametersToString[2, 1], "10");
+            Assert.AreEqual("Location (µ)", t.ParametersToString[0, 0]);
+            Assert.AreEqual("Scale (σ)", t.ParametersToString[1, 0]);
+            Assert.AreEqual("Degrees of Freedom (ν)", t.ParametersToString[2, 0]);
+            Assert.AreEqual("0", t.ParametersToString[0, 1]);
+            Assert.AreEqual("1", t.ParametersToString[1, 1]);
+            Assert.AreEqual("10", t.ParametersToString[2, 1]);
         }
 
         /// <summary>
@@ -173,10 +173,10 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Mean, 0);
+            Assert.AreEqual(0, t.Mean);
 
             var t2 = new StudentT(1, 1, 1);
-            Assert.AreEqual(t2.Mean, double.NaN);
+            Assert.AreEqual(double.NaN, t2.Mean);
         }
 
         /// <summary>
@@ -186,10 +186,10 @@ public void Test_Mean()
         public void Test_Median()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Median, 0);
+            Assert.AreEqual(0, t.Median);
 
             var t2 = new StudentT(1, 1, 1);
-            Assert.AreEqual(t2.Median, 1);
+            Assert.AreEqual(1, t2.Median);
         }
 
         /// <summary>
@@ -199,10 +199,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Mode, 0);
+            Assert.AreEqual(0, t.Mode);
 
             var t2 = new StudentT(1,1,1);
-            Assert.AreEqual(t2.Mode, 1);
+            Assert.AreEqual(1, t2.Mode);
         }
 
         /// <summary>
@@ -212,13 +212,13 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.StandardDeviation, 1.11803, 1e-04);
+            Assert.AreEqual(1.11803, t.StandardDeviation, 1e-04);
 
             var t2 = new StudentT(1, 1, 2);
-            Assert.AreEqual(t2.StandardDeviation,double.PositiveInfinity);
+            Assert.AreEqual(double.PositiveInfinity, t2.StandardDeviation);
 
             var t3 = new StudentT(1, 1, 1);
-            Assert.AreEqual(t3.StandardDeviation, double.NaN);
+            Assert.AreEqual(double.NaN, t3.StandardDeviation);
         }
 
         /// <summary>
@@ -228,10 +228,10 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Skewness, 0);
+            Assert.AreEqual(0, t.Skewness);
 
             var t2 = new StudentT(1, 1, 1);
-            Assert.AreEqual(t2.Skewness, double.NaN);
+            Assert.AreEqual(double.NaN,t2.Skewness );
         }
 
         /// <summary>
@@ -241,13 +241,13 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Kurtosis, 4);
+            Assert.AreEqual(4, t.Kurtosis );
 
             var t2 = new StudentT(1, 1, 4);
-            Assert.AreEqual(t2.Kurtosis, double.PositiveInfinity);
+            Assert.AreEqual(double.PositiveInfinity, t2.Kurtosis);
 
             var t3 = new StudentT(1, 1, 2);
-            Assert.AreEqual(t3.Kurtosis, double.NaN);
+            Assert.AreEqual(double.NaN, t3.Kurtosis);
         }
 
         /// <summary>
@@ -257,8 +257,8 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var t = new StudentT();
-            Assert.AreEqual(t.Minimum, double.NegativeInfinity);
-            Assert.AreEqual(t.Maximum, double.PositiveInfinity);
+            Assert.AreEqual(double.NegativeInfinity, t.Minimum);
+            Assert.AreEqual(double.PositiveInfinity, t.Maximum);
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_Triangular.cs b/Test_Numerics/Distributions/Univariate/Test_Triangular.cs
index 348e38db..49756414 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Triangular.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Triangular.cs
@@ -136,14 +136,14 @@ public void Test_Triangular_MLE()
         public void Test_Construction()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Min, 0);
-            Assert.AreEqual(T.Mode, 0.5);
-            Assert.AreEqual(T.Max, 1);
+            Assert.AreEqual(0,T.Min);
+            Assert.AreEqual(0.5, T.Mode);
+            Assert.AreEqual(1, T.Max);
 
             var T2 = new Triangular(-1,1,2);
-            Assert.AreEqual(T2.Min, -1);
-            Assert.AreEqual(T2.Mode, 1);
-            Assert.AreEqual(T2.Max, 2);
+            Assert.AreEqual(-1, T2.Min);
+            Assert.AreEqual(1, T2.Mode);
+            Assert.AreEqual(2, T2.Max);
         }
 
         /// <summary>
@@ -172,12 +172,12 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.ParametersToString[0, 0], "Min (a)");
-            Assert.AreEqual(T.ParametersToString[1, 0], "Most Likely (c)");
-            Assert.AreEqual(T.ParametersToString[2, 0], "Max (b)");
-            Assert.AreEqual(T.ParametersToString[0, 1], "0");
-            Assert.AreEqual(T.ParametersToString[1, 1], "0.5");
-            Assert.AreEqual(T.ParametersToString[2, 1], "1");
+            Assert.AreEqual("Min (a)",T.ParametersToString[0, 0] );
+            Assert.AreEqual("Most Likely (c)",T.ParametersToString[1, 0] );
+            Assert.AreEqual("Max (b)",T.ParametersToString[2, 0] );
+            Assert.AreEqual("0", T.ParametersToString[0, 1]);
+            Assert.AreEqual("0.5", T.ParametersToString[1, 1]);
+            Assert.AreEqual("1", T.ParametersToString[2, 1]);
         }
 
         /// <summary>
@@ -201,10 +201,10 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Mean, 0.5);
+            Assert.AreEqual(0.5, T.Mean);
 
             var T2 = new Triangular(1, 3, 6);
-            Assert.AreEqual(T2.Mean, 3.3333, 1e-04);
+            Assert.AreEqual(3.3333, T2.Mean,  1e-04);
         }
 
         /// <summary>
@@ -214,10 +214,10 @@ public void Test_Mean()
         public void Test_Median()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Median, 0.5);
+            Assert.AreEqual(0.5, T.Median);
 
             var T2 = new Triangular(1,3,6);
-            Assert.AreEqual(T2.Median, 3.26138, 1e-05);
+            Assert.AreEqual(3.26138, T2.Median,  1e-05);
         }
 
         /// <summary>
@@ -227,10 +227,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Mode, 0.5);
+            Assert.AreEqual(0.5, T.Mode);
 
             var T2 = new Triangular(1, 3, 6);
-            Assert.AreEqual(T2.Mode, 3);
+            Assert.AreEqual(3, T2.Mode);
         }
 
         /// <summary>
@@ -240,10 +240,10 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.StandardDeviation, 0.20412, 1e-04);
+            Assert.AreEqual(0.20412, T.StandardDeviation, 1e-04);
 
             var T2 = new Triangular(1, 3, 6);
-            Assert.AreEqual(T2.StandardDeviation, 1.02739, 1e-04);
+            Assert.AreEqual(1.02739, T2.StandardDeviation,  1e-04);
         }
 
         /// <summary>
@@ -253,7 +253,7 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Skewness, 0);
+            Assert.AreEqual(0, T.Skewness);
         }
 
         /// <summary>
@@ -263,10 +263,10 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Kurtosis, 12d / 5d);
+            Assert.AreEqual(12d / 5d, T.Kurtosis);
 
             var T2 = new Triangular(1, 3, 6);
-            Assert.AreEqual(T2.Kurtosis, 12d / 5d);
+            Assert.AreEqual(12d / 5d, T2.Kurtosis);
         }
 
         /// <summary>
@@ -276,12 +276,12 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.Minimum, 0);
-            Assert.AreEqual(T.Maximum, 1);
+            Assert.AreEqual(0, T.Minimum);
+            Assert.AreEqual(1, T.Maximum);
 
             var T2 = new Triangular(1, 3, 6);
-            Assert.AreEqual(T2.Minimum, 1);
-            Assert.AreEqual(T2.Maximum, 6);
+            Assert.AreEqual(1, T2.Minimum);
+            Assert.AreEqual(6, T2.Maximum);
         }
 
         /// <summary>
@@ -291,13 +291,13 @@ public void Test_MinMax()
         public void Test_PDF()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.PDF(-1), 0);
-            Assert.AreEqual(T.PDF(0.4), 1.6);
-            Assert.AreEqual(T.PDF(0.6), 1.6);
-            Assert.AreEqual(T.PDF(0.5), 2);
+            Assert.AreEqual(0,T.PDF(-1));
+            Assert.AreEqual(1.6, T.PDF(0.4));
+            Assert.AreEqual(1.6, T.PDF(0.6));
+            Assert.AreEqual(2, T.PDF(0.5));
 
             var T2 = new Triangular(1, 3, 6);
-            Assert.AreEqual(T2.PDF(2), 0.2, 1e-04);
+            Assert.AreEqual(0.2, T2.PDF(2),  1e-04);
         }
 
         /// <summary>
@@ -307,13 +307,13 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.CDF(-1), 0);
-            Assert.AreEqual(T.CDF(2), 1);
-            Assert.AreEqual(T.CDF(0.4), 0.32,1e-04);
-            Assert.AreEqual(T.CDF(0.6), 0.68,1e-04);
+            Assert.AreEqual(0, T.CDF(-1));
+            Assert.AreEqual(1, T.CDF(2));
+            Assert.AreEqual(0.32, T.CDF(0.4), 1e-04);
+            Assert.AreEqual(0.68, T.CDF(0.6), 1e-04);
 
             var T2 = new Triangular(1,3, 6);
-            Assert.AreEqual(T2.CDF(2), 0.1, 1e-04);
+            Assert.AreEqual(0.1, T2.CDF(2),  1e-04);
         }
 
         /// <summary>
@@ -323,10 +323,10 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var T = new Triangular();
-            Assert.AreEqual(T.InverseCDF(0), 0);
-            Assert.AreEqual(T.InverseCDF(1), 1);
-            Assert.AreEqual(T.InverseCDF(0.2), 0.31622, 1e-04);
-            Assert.AreEqual(T.InverseCDF(0.5), 0.5);
+            Assert.AreEqual(0, T.InverseCDF(0));
+            Assert.AreEqual(1,T.InverseCDF(1));
+            Assert.AreEqual(0.31622, T.InverseCDF(0.2),  1e-04);
+            Assert.AreEqual(0.5, T.InverseCDF(0.5));
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_TruncatedDistribution.cs b/Test_Numerics/Distributions/Univariate/Test_TruncatedDistribution.cs
index 7bf07f0f..9af0500e 100644
--- a/Test_Numerics/Distributions/Univariate/Test_TruncatedDistribution.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_TruncatedDistribution.cs
@@ -59,27 +59,27 @@ public void Test_TruncatedNormalDist()
             var p = tn.CDF(1.5);
             var q = tn.InverseCDF(p);
 
-            Assert.AreEqual(d, 0.9786791, 1E-5);
-            Assert.AreEqual(p, 0.3460251, 1E-5);
-            Assert.AreEqual(q, 1.5, 1E-5);
+            Assert.AreEqual(0.9786791, d,  1E-5);
+            Assert.AreEqual(0.3460251, p,  1E-5);
+            Assert.AreEqual(1.5, q,  1E-5);
 
             tn = new TruncatedDistribution(new Normal(10, 3), 8, 25);
             d = tn.PDF(12.75);
             p = tn.CDF(12.75);
             q = tn.InverseCDF(p);
 
-            Assert.AreEqual(d, 0.1168717, 1E-5);
-            Assert.AreEqual(p, 0.7596566, 1E-5);
-            Assert.AreEqual(q, 12.75, 1E-5);
+            Assert.AreEqual(0.1168717, d,  1E-5);
+            Assert.AreEqual(0.7596566, p,  1E-5);
+            Assert.AreEqual(12.75, q,  1E-5);
 
             tn = new TruncatedDistribution(new Normal(0, 3), 0, 9);
             d = tn.PDF(4.5);
             p = tn.CDF(4.5);
             q = tn.InverseCDF(p);
 
-            Assert.AreEqual(d, 0.08657881, 1E-5);
-            Assert.AreEqual(p, 0.868731, 1E-5);
-            Assert.AreEqual(q, 4.5, 1E-5);
+            Assert.AreEqual(0.08657881, d,  1E-5);
+            Assert.AreEqual(0.868731, p,  1E-5);
+            Assert.AreEqual(4.5, q,  1E-5);
 
         }
 
@@ -90,16 +90,16 @@ public void Test_TruncatedNormalDist()
         public void Test_Construction()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(((Normal)tn.BaseDistribution).Mu, 0.5);
-            Assert.AreEqual(((Normal)tn.BaseDistribution).Sigma, 0.2);
-            Assert.AreEqual(tn.Min, 0);
-            Assert.AreEqual(tn.Max, 1);
+            Assert.AreEqual(0.5,((Normal)tn.BaseDistribution).Mu);
+            Assert.AreEqual(0.2, ((Normal)tn.BaseDistribution).Sigma);
+            Assert.AreEqual(0, tn.Min);
+            Assert.AreEqual(1, tn.Max);
 
             var tn2 = new TruncatedDistribution(new Normal(1, 1), 1, 2);
-            Assert.AreEqual(((Normal)tn2.BaseDistribution).Mu, 1);
-            Assert.AreEqual(((Normal)tn2.BaseDistribution).Sigma, 1);
-            Assert.AreEqual(tn2.Min, 1);
-            Assert.AreEqual(tn2.Max, 2);
+            Assert.AreEqual(1, ((Normal)tn2.BaseDistribution).Mu);
+            Assert.AreEqual(1, ((Normal)tn2.BaseDistribution).Sigma);
+            Assert.AreEqual(1, tn2.Min);
+            Assert.AreEqual(2, tn2.Max);
         }
 
         /// <summary>
@@ -109,14 +109,14 @@ public void Test_Construction()
         public void Test_ParametersToString()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.ParametersToString[0, 0], "Mean (µ)");
-            Assert.AreEqual(tn.ParametersToString[1, 0], "Std Dev (σ)");
-            Assert.AreEqual(tn.ParametersToString[2, 0], "Min");
-            Assert.AreEqual(tn.ParametersToString[3, 0], "Max");
-            Assert.AreEqual(tn.ParametersToString[0, 1], "0.5");
-            Assert.AreEqual(tn.ParametersToString[1, 1], "0.2");
-            Assert.AreEqual(tn.ParametersToString[2, 1], "0");
-            Assert.AreEqual(tn.ParametersToString[3, 1], "1");
+            Assert.AreEqual("Mean (µ)",tn.ParametersToString[0, 0]);
+            Assert.AreEqual("Std Dev (σ)", tn.ParametersToString[1, 0]);
+            Assert.AreEqual("Min", tn.ParametersToString[2, 0]);
+            Assert.AreEqual("Max", tn.ParametersToString[3, 0]);
+            Assert.AreEqual("0.5", tn.ParametersToString[0, 1]);
+            Assert.AreEqual("0.2", tn.ParametersToString[1, 1]);
+            Assert.AreEqual("0", tn.ParametersToString[2, 1]);
+            Assert.AreEqual("1", tn.ParametersToString[3, 1]);
         }
 
         /// <summary>
@@ -140,7 +140,7 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.Mean, 0.5, 1e-4);
+            Assert.AreEqual(0.5, tn.Mean, 1e-4);
         }
 
         /// <summary>
@@ -150,7 +150,7 @@ public void Test_Mean()
         public void Test_Median()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.Median, 0.5, 1e-4);
+            Assert.AreEqual(0.5, tn.Median,  1e-4);
         }
 
         /// <summary>
@@ -160,7 +160,7 @@ public void Test_Median()
         public void Test_Mode()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.Mode, 0.5, 1e-4);
+            Assert.AreEqual(0.5, tn.Mode, 1e-4);
         }
 
         /// <summary>
@@ -170,7 +170,7 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.StandardDeviation, 0.19091, 1e-4);
+            Assert.AreEqual(0.19091, tn.StandardDeviation,  1e-4);
         }
 
         /// <summary>
@@ -180,7 +180,7 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.Skewness, 0, 1E-4);
+            Assert.AreEqual(0, tn.Skewness,  1E-4);
         }
 
         /// <summary>
@@ -190,7 +190,7 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.Kurtosis, 2.62422, 1e-04);
+            Assert.AreEqual(2.62422, tn.Kurtosis,  1e-04);
         }
 
         /// <summary>
@@ -200,8 +200,8 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var tn = new TruncatedDistribution(new Normal(0.5, 0.2), 0, 1);
-            Assert.AreEqual(tn.Minimum, 0);
-            Assert.AreEqual(tn.Maximum, 1);
+            Assert.AreEqual(0, tn.Minimum);
+            Assert.AreEqual(1, tn.Maximum);
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_TruncatedNormal.cs b/Test_Numerics/Distributions/Univariate/Test_TruncatedNormal.cs
index d7fb8b80..92d1a714 100644
--- a/Test_Numerics/Distributions/Univariate/Test_TruncatedNormal.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_TruncatedNormal.cs
@@ -66,27 +66,27 @@ public void Test_TruncatedNormalDist()
             var p = tn.CDF(1.5);
             var q = tn.InverseCDF(p);
 
-            Assert.AreEqual(d, 0.9786791, 1E-5);
-            Assert.AreEqual(p, 0.3460251, 1E-5);
-            Assert.AreEqual(q, 1.5, 1E-5);
+            Assert.AreEqual(0.9786791, d,  1E-5);
+            Assert.AreEqual(0.3460251, p,  1E-5);
+            Assert.AreEqual(1.5, q,  1E-5);
 
             tn = new TruncatedNormal(10, 3, 8, 25);
             d = tn.PDF(12.75);
             p = tn.CDF(12.75);
             q = tn.InverseCDF(p);
 
-            Assert.AreEqual(d, 0.1168717, 1E-5);
-            Assert.AreEqual(p, 0.7596566, 1E-5);
-            Assert.AreEqual(q, 12.75, 1E-5);
+            Assert.AreEqual(0.1168717, d ,1E-5);
+            Assert.AreEqual(0.7596566, p, 1E-5);
+            Assert.AreEqual(12.75, q, 1E-5);
 
             tn = new TruncatedNormal(0, 3, 0, 9);
             d = tn.PDF(4.5);
             p = tn.CDF(4.5);
             q = tn.InverseCDF(p);
 
-            Assert.AreEqual(d, 0.08657881, 1E-5);
-            Assert.AreEqual(p, 0.868731, 1E-5);
-            Assert.AreEqual(q, 4.5, 1E-5);
+            Assert.AreEqual(0.08657881, d, 1E-5);
+            Assert.AreEqual(0.868731, p, 1E-5);
+            Assert.AreEqual(4.5, q, 1E-5);
 
         }
 
@@ -97,16 +97,16 @@ public void Test_TruncatedNormalDist()
         public void Test_Construction()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Mu, 0.5);
-            Assert.AreEqual(tn.Sigma, 0.2);
-            Assert.AreEqual(tn.Min, 0);
-            Assert.AreEqual(tn.Max, 1);
+            Assert.AreEqual(0.5,tn.Mu );
+            Assert.AreEqual(0.2,tn.Sigma );
+            Assert.AreEqual(0,tn.Min);
+            Assert.AreEqual(1,tn.Max);
 
             var tn2 = new TruncatedNormal(1, 1, 1, 2);
-            Assert.AreEqual(tn2.Mu, 1);
-            Assert.AreEqual(tn2.Sigma, 1);
-            Assert.AreEqual(tn2.Min, 1);
-            Assert.AreEqual(tn2.Max, 2);
+            Assert.AreEqual(1,tn2.Mu);
+            Assert.AreEqual(1,tn2.Sigma);
+            Assert.AreEqual(1,tn2.Min);
+            Assert.AreEqual(2,tn2.Max);
         }
 
         /// <summary>
@@ -135,14 +135,14 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.ParametersToString[0, 0], "Mean (µ)");
-            Assert.AreEqual(tn.ParametersToString[1, 0], "Std Dev (σ)");
-            Assert.AreEqual(tn.ParametersToString[2, 0], "Min");
-            Assert.AreEqual(tn.ParametersToString[3, 0], "Max");
-            Assert.AreEqual(tn.ParametersToString[0, 1], "0.5");
-            Assert.AreEqual(tn.ParametersToString[1, 1], "0.2");
-            Assert.AreEqual(tn.ParametersToString[2, 1], "0");
-            Assert.AreEqual(tn.ParametersToString[3, 1], "1");
+            Assert.AreEqual("Mean (µ)",tn.ParametersToString[0, 0]);
+            Assert.AreEqual("Std Dev (σ)",tn.ParametersToString[1, 0]);
+            Assert.AreEqual("Min",tn.ParametersToString[2, 0] );
+            Assert.AreEqual("Max", tn.ParametersToString[3, 0]  );
+            Assert.AreEqual("0.5",tn.ParametersToString[0, 1]);
+            Assert.AreEqual("0.2", tn.ParametersToString[1, 1]);
+            Assert.AreEqual("0", tn.ParametersToString[2, 1]);
+            Assert.AreEqual("1", tn.ParametersToString[3, 1]);
         }
 
         /// <summary>
@@ -166,7 +166,7 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Mean, 0.5);
+            Assert.AreEqual(0.5,tn.Mean);
         }
 
         /// <summary>
@@ -176,7 +176,7 @@ public void Test_Mean()
         public void Test_Median()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Median, 0.5);
+            Assert.AreEqual(0.5, tn.Median);
         }
 
         /// <summary>
@@ -186,7 +186,7 @@ public void Test_Median()
         public void Test_Mode()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Mode, 0.5);
+            Assert.AreEqual(0.5, tn.Mode);
         }
 
         /// <summary>
@@ -196,7 +196,7 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.StandardDeviation, 0.19091,1e-05);
+            Assert.AreEqual(0.19091, tn.StandardDeviation, 1e-05);
         }
 
         /// <summary>
@@ -206,7 +206,7 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Skewness, 0);
+            Assert.AreEqual(0, tn.Skewness);
         }
 
         /// <summary>
@@ -216,7 +216,7 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Kurtosis, 2.62422, 1e-04);
+            Assert.AreEqual(2.62422, tn.Kurtosis,  1e-04);
         }
 
         /// <summary>
@@ -226,8 +226,8 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var tn = new TruncatedNormal();
-            Assert.AreEqual(tn.Minimum, 0);
-            Assert.AreEqual(tn.Maximum, 1);
+            Assert.AreEqual(0, tn.Minimum);
+            Assert.AreEqual(1, tn.Maximum);
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_Uniform.cs b/Test_Numerics/Distributions/Univariate/Test_Uniform.cs
index 7b2cd14d..56f17684 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Uniform.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Uniform.cs
@@ -105,12 +105,12 @@ public void Test_Uniform_R()
         public void Test_Construction()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Min, 0);
-            Assert.AreEqual(U.Max, 1);
+            Assert.AreEqual(0,U.Min);
+            Assert.AreEqual(1, U.Max);
 
             var U2 = new Uniform(2,10);
-            Assert.AreEqual(U2.Min, 2); 
-            Assert.AreEqual(U2.Max, 10);
+            Assert.AreEqual(2, U2.Min); 
+            Assert.AreEqual(10, U2.Max);
         }
 
         /// <summary>
@@ -142,10 +142,10 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.ParametersToString[0, 0], "Min");
-            Assert.AreEqual(U.ParametersToString[1, 0], "Max");
-            Assert.AreEqual(U.ParametersToString[0, 1], "0");
-            Assert.AreEqual(U.ParametersToString[1, 1], "1");
+            Assert.AreEqual("Min",U.ParametersToString[0, 0]);
+            Assert.AreEqual("Max", U.ParametersToString[1, 0]);
+            Assert.AreEqual("0", U.ParametersToString[0, 1]);
+            Assert.AreEqual("1", U.ParametersToString[1, 1]);
         }
 
         /// <summary>
@@ -169,10 +169,10 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Mean, 0.5);
+            Assert.AreEqual(0.5, U.Mean);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.Mean, 6);
+            Assert.AreEqual(6, U2.Mean);
         }
 
         /// <summary>
@@ -182,10 +182,10 @@ public void Test_Mean()
         public void Test_Median()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Median, 0.5);
+            Assert.AreEqual(0.5, U.Median);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.Median, 6);
+            Assert.AreEqual(6, U2.Median);
         }
 
         /// <summary>
@@ -195,10 +195,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Mode,double.NaN);
+            Assert.AreEqual(double.NaN,U.Mode);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.Mode,double.NaN);
+            Assert.AreEqual(double.NaN, U2.Mode);
         }
 
         /// <summary>
@@ -208,10 +208,10 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.StandardDeviation, 0.288675, 1e-05);
+            Assert.AreEqual(0.288675, U.StandardDeviation,  1e-05);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.StandardDeviation, 2.3094, 1e-04);
+            Assert.AreEqual(2.3094, U2.StandardDeviation, 1e-04);
         }
 
         /// <summary>
@@ -221,10 +221,10 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Skewness, 0);
+            Assert.AreEqual(0, U.Skewness);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.Skewness, 0);
+            Assert.AreEqual(0, U2.Skewness);
         }
 
         /// <summary>
@@ -234,10 +234,10 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Kurtosis, 9d / 5d);
+            Assert.AreEqual(9d / 5d,U.Kurtosis);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.Kurtosis, 9d / 5d);
+            Assert.AreEqual(9d / 5d, U2.Kurtosis);
         }
 
         /// <summary>
@@ -247,12 +247,12 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.Minimum, 0);
-            Assert.AreEqual(U.Maximum, 1);
+            Assert.AreEqual(0, U.Minimum);
+            Assert.AreEqual(1, U.Maximum);
 
             var U2 = new Uniform(2, 10);
-            Assert.AreEqual(U2.Minimum, 2);
-            Assert.AreEqual(U2.Maximum, 10);
+            Assert.AreEqual(2, U2.Minimum);
+            Assert.AreEqual(10, U2.Maximum);
         }
 
         /// <summary>
@@ -262,9 +262,9 @@ public void Test_MinMax()
         public void Test_PDF()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.PDF(-1),0);
-            Assert.AreEqual(U.PDF(2),0);
-            Assert.AreEqual(U.PDF(1), 1);
+            Assert.AreEqual(0, U.PDF(-1));
+            Assert.AreEqual(0, U.PDF(2));
+            Assert.AreEqual(1, U.PDF(1));
         }
 
         /// <summary>
@@ -274,9 +274,9 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.CDF(0),0);
-            Assert.AreEqual(U.CDF(1),1);
-            Assert.AreEqual(U.CDF(0.5), 0.5);
+            Assert.AreEqual(0,U.CDF(0));
+            Assert.AreEqual(1,U.CDF(1));
+            Assert.AreEqual(0.5,U.CDF(0.5));
         }
 
         /// <summary>
@@ -286,9 +286,9 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var U = new Uniform();
-            Assert.AreEqual(U.InverseCDF(0), 0);
-            Assert.AreEqual(U.InverseCDF(1), 1);
-            Assert.AreEqual(U.InverseCDF(0.3), 0.3);        
+            Assert.AreEqual(0,U.InverseCDF(0));
+            Assert.AreEqual(1,U.InverseCDF(1));
+            Assert.AreEqual(0.3,U.InverseCDF(0.3));        
         }
     }
 }
diff --git a/Test_Numerics/Distributions/Univariate/Test_UniformDiscrete.cs b/Test_Numerics/Distributions/Univariate/Test_UniformDiscrete.cs
index bb12c566..60264d36 100644
--- a/Test_Numerics/Distributions/Univariate/Test_UniformDiscrete.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_UniformDiscrete.cs
@@ -90,12 +90,12 @@ public void Test_UniformDiscreteDist()
         public void Test_Construction()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Min, 0);
-            Assert.AreEqual(U.Max, 1);
+            Assert.AreEqual(0,U.Min);
+            Assert.AreEqual(1,U.Max);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Min, 2);
-            Assert.AreEqual(U2.Max, 10);
+            Assert.AreEqual(2, U2.Min);
+            Assert.AreEqual(10, U2.Max);
         }
 
         /// <summary>
@@ -127,10 +127,10 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.ParametersToString[0, 0], "Min");
-            Assert.AreEqual(U.ParametersToString[1, 0], "Max");
-            Assert.AreEqual(U.ParametersToString[0, 1], "0");
-            Assert.AreEqual(U.ParametersToString[1, 1], "1");
+            Assert.AreEqual("Min",U.ParametersToString[0, 0] );
+            Assert.AreEqual("Max", U.ParametersToString[1, 0]);
+            Assert.AreEqual("0", U.ParametersToString[0, 1]);
+            Assert.AreEqual("1", U.ParametersToString[1, 1]);
         }
 
         /// <summary>
@@ -140,10 +140,10 @@ public void Test_ParametersToString()
         public void Test_Mean()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Mean, 0.5);
+            Assert.AreEqual(0.5, U.Mean);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Mean, 6);
+            Assert.AreEqual(6, U2.Mean);
         }
 
         /// <summary>
@@ -153,10 +153,10 @@ public void Test_Mean()
         public void Test_Median()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Median, 0.5);
+            Assert.AreEqual(0.5, U.Median);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Median, 6);
+            Assert.AreEqual(6, U2.Median);
         }
 
         /// <summary>
@@ -166,10 +166,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Mode, double.NaN);
+            Assert.AreEqual(double.NaN,U.Mode);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Mode, double.NaN);
+            Assert.AreEqual(double.NaN, U2.Mode);
         }
 
         /// <summary>
@@ -179,10 +179,10 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.StandardDeviation, 0.288675, 1e-05);
+            Assert.AreEqual(0.288675, U.StandardDeviation,  1e-05);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.StandardDeviation, 2.3094, 1e-04);
+            Assert.AreEqual(2.3094, U2.StandardDeviation,  1e-04);
         }
 
         /// <summary>
@@ -192,10 +192,10 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Skewness, 0);
+            Assert.AreEqual(0, U.Skewness);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Skewness, 0);
+            Assert.AreEqual(0, U2.Skewness);
         }
 
         /// <summary>
@@ -205,10 +205,10 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Kurtosis, 1);
+            Assert.AreEqual(1, U.Kurtosis);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Kurtosis, 1.77);
+            Assert.AreEqual(1.77, U2.Kurtosis);
         }
 
         /// <summary>
@@ -218,12 +218,12 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.Minimum, 0);
-            Assert.AreEqual(U.Maximum, 1);
+            Assert.AreEqual(0, U.Minimum);
+            Assert.AreEqual(1, U.Maximum);
 
             var U2 = new UniformDiscrete(2, 10);
-            Assert.AreEqual(U2.Minimum, 2);
-            Assert.AreEqual(U2.Maximum, 10);
+            Assert.AreEqual(2, U2.Minimum);
+            Assert.AreEqual(10, U2.Maximum);
         }
 
         /// <summary>
@@ -233,9 +233,9 @@ public void Test_MinMax()
         public void Test_PDF()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.PDF(-1), 0);
-            Assert.AreEqual(U.PDF(2), 0);
-            Assert.AreEqual(U.PDF(1), 0.5);
+            Assert.AreEqual(0, U.PDF(-1));
+            Assert.AreEqual(0, U.PDF(2));
+            Assert.AreEqual(0.5, U.PDF(1));
         }
 
         /// <summary>
@@ -245,9 +245,9 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.CDF(0), 0.5);
-            Assert.AreEqual(U.CDF(1), 1);
-            Assert.AreEqual(U.CDF(0.5), 0.75);
+            Assert.AreEqual(0.5, U.CDF(0));
+            Assert.AreEqual(1, U.CDF(1));
+            Assert.AreEqual(0.75, U.CDF(0.5));
         }
 
         /// <summary>
@@ -257,9 +257,9 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var U = new UniformDiscrete();
-            Assert.AreEqual(U.InverseCDF(0), 0);
-            Assert.AreEqual(U.InverseCDF(1), 1);
-            Assert.AreEqual(U.InverseCDF(0.3), 0);
+            Assert.AreEqual(0, U.InverseCDF(0));
+            Assert.AreEqual(1,U.InverseCDF(1));
+            Assert.AreEqual(0,U.InverseCDF(0.3));
         }
     }
 
diff --git a/Test_Numerics/Distributions/Univariate/Test_Weibull.cs b/Test_Numerics/Distributions/Univariate/Test_Weibull.cs
index 1b9ea373..79284819 100644
--- a/Test_Numerics/Distributions/Univariate/Test_Weibull.cs
+++ b/Test_Numerics/Distributions/Univariate/Test_Weibull.cs
@@ -71,8 +71,8 @@ public void Test_Weibull_MLE_Fit()
             double kappa = W.Kappa;
             double true_L = 9.589d;
             double true_k = 1.907d;
-            Assert.AreEqual((lamda - true_L) / true_L < 0.01d, true);
-            Assert.AreEqual((kappa - true_k) / true_k < 0.01d, true);
+            Assert.IsLessThan(0.01d,(lamda - true_L) / true_L);
+            Assert.IsLessThan(0.01d,(kappa - true_k) / true_k);
         }
 
         /// <summary>
@@ -92,10 +92,10 @@ public void Test_Weibull_Quantile()
             var W = new Weibull(9.589d, 1.907d);
             double q100 = W.InverseCDF(0.99d);
             double true_q100 = 21.358d;
-            Assert.AreEqual((q100 - true_q100) / true_q100 < 0.01d, true);
+            Assert.IsLessThan(0.01d, (q100 - true_q100) / true_q100);
             double p = W.CDF(q100);
             double true_p = 0.99d;
-            Assert.AreEqual((p - true_p) / true_p < 0.01d, true);
+            Assert.IsLessThan(0.01d, (p - true_p) / true_p);
         }
 
         /// <summary>
@@ -117,7 +117,7 @@ public void Test_Weibull_StandardError()
             var GUM = new Gumbel(8049.6d, 4478.6d);
             double qVar99 = Math.Sqrt(GUM.QuantileVariance(0.99d, 53, ParameterEstimationMethod.MaximumLikelihood));
             double true_qVar99 = 2486.5d;
-            Assert.AreEqual((qVar99 - true_qVar99) / true_qVar99 < 0.01d, true);
+            Assert.IsLessThan(0.01d, (qVar99 - true_qVar99) / true_qVar99);
         }
 
         /// <summary>
@@ -137,8 +137,8 @@ public void Test_Weibull_GOF()
                 modeled[i] = W.CDF(sample[i]);
             double true_AIC = 294.5878d;
             double true_BIC = 298.1566d;
-            Assert.AreEqual((AIC - true_AIC) / true_AIC < 0.01d, true);
-            Assert.AreEqual((BIC - true_BIC) / true_BIC < 0.01d, true);
+            Assert.IsLessThan(0.01d, (AIC - true_AIC) / true_AIC);
+            Assert.IsLessThan(0.01d, (BIC - true_BIC) / true_BIC);
         }
 
         /// <summary>
@@ -148,12 +148,12 @@ public void Test_Weibull_GOF()
         public void Test_Construction()
         {
             var W = new Weibull();
-            Assert.AreEqual(W.Lambda, 10);
-            Assert.AreEqual(W.Kappa, 2);
+            Assert.AreEqual(10,W.Lambda);
+            Assert.AreEqual(2, W.Kappa);
 
             var W2 = new Weibull(1, 1);
-            Assert.AreEqual(W2.Lambda, 1);
-            Assert.AreEqual(W2.Kappa, 1);
+            Assert.AreEqual(1, W2.Lambda);
+            Assert.AreEqual(1, W2.Kappa);
         }
 
         /// <summary>
@@ -182,10 +182,10 @@ public void Test_InvalidParameters()
         public void Test_ParametersToString()
         {
             var W = new Weibull();
-            Assert.AreEqual(W.ParametersToString[0, 0], "Scale (λ)");
-            Assert.AreEqual(W.ParametersToString[1, 0], "Shape (κ)");
-            Assert.AreEqual(W.ParametersToString[0, 1], "10");
-            Assert.AreEqual(W.ParametersToString[1, 1], "2");
+            Assert.AreEqual("Scale (λ)",W.ParametersToString[0, 0] );
+            Assert.AreEqual("Shape (κ)", W.ParametersToString[1, 0]);
+            Assert.AreEqual("10", W.ParametersToString[0, 1]);
+            Assert.AreEqual("2", W.ParametersToString[1, 1]);
         }
 
         /// <summary>
@@ -209,10 +209,10 @@ public void Test_Moments()
         public void Test_Mean()
         {
             var W = new Weibull(0.1, 1);
-            Assert.AreEqual(W.Mean, 0.1);
+            Assert.AreEqual(0.1, W.Mean);
 
             var W2 = new Weibull(1, 1);
-            Assert.AreEqual(W2.Mean, 1);
+            Assert.AreEqual(1, W2.Mean);
         }
 
         /// <summary>
@@ -222,10 +222,10 @@ public void Test_Mean()
         public void Test_Median()
         {
             var W = new Weibull(0.1, 1);
-            Assert.AreEqual(W.Median, 0.06931, 1e-04);
+            Assert.AreEqual(0.06931, W.Median,  1e-04);
 
             var W2 = new Weibull(1, 1);
-            Assert.AreEqual(W2.Median, 0.69314, 1e-04);
+            Assert.AreEqual(0.69314, W2.Median,  1e-04);
         }
 
         /// <summary>
@@ -235,10 +235,10 @@ public void Test_Median()
         public void Test_Mode()
         {
             var W = new Weibull(0.1, 1);
-            Assert.AreEqual(W.Mode, 0);
+            Assert.AreEqual(0, W.Mode);
 
             var W2 = new Weibull(10, 10);
-            Assert.AreEqual(W2.Mode, 9.89519, 1e-05);
+            Assert.AreEqual(9.89519, W2.Mode,  1e-05);
         }
 
         /// <summary>
@@ -248,10 +248,10 @@ public void Test_Mode()
         public void Test_StandardDeviation()
         {
             var W = new Weibull(0.1, 1);
-            Assert.AreEqual(W.StandardDeviation, 0.1);
+            Assert.AreEqual(0.1, W.StandardDeviation);
 
             var W2 = new Weibull(1, 1);
-            Assert.AreEqual(W2.StandardDeviation, 1);
+            Assert.AreEqual(1, W2.StandardDeviation);
         }
 
         /// <summary>
@@ -261,10 +261,10 @@ public void Test_StandardDeviation()
         public void Test_Skewness()
         {
             var W = new Weibull(0.1, 1);
-            Assert.AreEqual(W.Skewness, 2,1e-04);
+            Assert.AreEqual(2, W.Skewness, 1e-04);
 
             var W2 = new Weibull(1, 1);
-            Assert.AreEqual(W2.Skewness, 2);
+            Assert.AreEqual(2, W2.Skewness);
         }
 
         /// <summary>
@@ -274,10 +274,10 @@ public void Test_Skewness()
         public void Test_Kurtosis()
         {
             var W = new Weibull();
-            Assert.AreEqual(W.Kurtosis, 3.24508,1e-04);
+            Assert.AreEqual(3.24508, W.Kurtosis,1e-04);
 
             var W2 = new Weibull(1, 1);
-            Assert.AreEqual(W2.Kurtosis, 9);
+            Assert.AreEqual(9, W2.Kurtosis);
         }
 
         /// <summary>
@@ -287,8 +287,8 @@ public void Test_Kurtosis()
         public void Test_MinMax()
         {
             var W = new Weibull();
-            Assert.AreEqual(W.Minimum, 0);
-            Assert.AreEqual(W.Maximum,double.PositiveInfinity);
+            Assert.AreEqual(0, W.Minimum);
+            Assert.AreEqual(double.PositiveInfinity,W.Maximum);
         }
 
         /// <summary>
@@ -298,9 +298,9 @@ public void Test_MinMax()
         public void Test_PDF()
         {
             var W = new Weibull(1, 1);
-            Assert.AreEqual(W.PDF(0), 1);
-            Assert.AreEqual(W.PDF(1), 0.36787, 1e-05);
-            Assert.AreEqual(W.PDF(10), 0.00004539, 1e-08);
+            Assert.AreEqual(1, W.PDF(0));
+            Assert.AreEqual(0.36787, W.PDF(1),  1e-05);
+            Assert.AreEqual(0.00004539, W.PDF(10), 1e-08);
         }
 
         /// <summary>
@@ -310,9 +310,9 @@ public void Test_PDF()
         public void Test_CDF()
         {
             var W = new Weibull(1, 1);
-            Assert.AreEqual(W.CDF(0), 0);
-            Assert.AreEqual(W.CDF(1), 0.63212, 1e-05);
-            Assert.AreEqual(W.CDF(10), 0.99995, 1e-05);
+            Assert.AreEqual(0, W.CDF(0));
+            Assert.AreEqual(0.63212, W.CDF(1), 1e-05);
+            Assert.AreEqual(0.99995, W.CDF(10), 1e-05);
         }
 
         /// <summary>
@@ -322,9 +322,9 @@ public void Test_CDF()
         public void Test_InverseCDF()
         {
             var W = new Weibull();
-            Assert.AreEqual(W.InverseCDF(0),0);
-            Assert.AreEqual(W.InverseCDF(1),double.PositiveInfinity);
-            Assert.AreEqual(W.InverseCDF(0.4), 7.1472, 1e-04);
+            Assert.AreEqual(0,W.InverseCDF(0));
+            Assert.AreEqual(double.PositiveInfinity,W.InverseCDF(1));
+            Assert.AreEqual(7.1472, W.InverseCDF(0.4),  1e-04);
         }
     }
 }
diff --git a/Test_Numerics/Functions/Test_Functions.cs b/Test_Numerics/Functions/Test_Functions.cs
index 3028e140..06cf0a27 100644
--- a/Test_Numerics/Functions/Test_Functions.cs
+++ b/Test_Numerics/Functions/Test_Functions.cs
@@ -232,12 +232,12 @@ public void Test_Tabular_Function()
             // Given X
             double X = 50.0;
             double Y = func.Function(X);
-            Assert.AreEqual(Y, 100.0);
+            Assert.AreEqual(100.0,Y);
 
             // Given Y
             double Y2 = 100d;
             double X2 = func.InverseFunction(Y2);
-            Assert.AreEqual(X, 50.0);
+            Assert.AreEqual(50.0, X);
 
             // Given X - Interpolation
             double X3 = 75.0d;
diff --git a/Test_Numerics/Machine Learning/Supervised/Test_DecisionTree.cs b/Test_Numerics/Machine Learning/Supervised/Test_DecisionTree.cs
index 46a67fe8..ec39e56c 100644
--- a/Test_Numerics/Machine Learning/Supervised/Test_DecisionTree.cs	
+++ b/Test_Numerics/Machine Learning/Supervised/Test_DecisionTree.cs	
@@ -90,7 +90,7 @@ public void Test_DecisionTree_Iris()
             var accuracy = GoodnessOfFit.Accuracy(Y_test.Array, prediction);
 
             // Accuracy should be greater than or equal to 90%
-            Assert.IsGreaterThanOrEqualTo(90,accuracy );
+            Assert.IsGreaterThanOrEqualTo(90,accuracy);
 
         }
 
diff --git a/Test_Numerics/Machine Learning/Supervised/Test_RandomForest.cs b/Test_Numerics/Machine Learning/Supervised/Test_RandomForest.cs
index d79c48e7..e779599d 100644
--- a/Test_Numerics/Machine Learning/Supervised/Test_RandomForest.cs	
+++ b/Test_Numerics/Machine Learning/Supervised/Test_RandomForest.cs	
@@ -133,7 +133,7 @@ public void Test_RandomForest_Regression()
             var lmR2 = GoodnessOfFit.RSquared(Y_test.Array, lmPredict);
 
             // Random Forest is better
-            Assert.IsGreaterThan(lmR2, rfR2 );
+            Assert.IsGreaterThan(lmR2,rfR2);
 
         }
     }
diff --git a/Test_Numerics/Mathematics/Integration/Test_Vegas.cs b/Test_Numerics/Mathematics/Integration/Test_Vegas.cs
index bfd47fae..1c25940e 100644
--- a/Test_Numerics/Mathematics/Integration/Test_Vegas.cs
+++ b/Test_Numerics/Mathematics/Integration/Test_Vegas.cs
@@ -209,12 +209,12 @@ public void Test_PowerTransform_RareUpperTailEvent()
 
             // Analytical approximation: P(Sum > mean + 3σ) = P(Z > 3) ≈ 0.00135
             // We expect something in the ballpark of 1e-3 to 2e-3
-            Assert.IsGreaterThan(5E-4, failureProbability );
-            Assert.IsLessThan(5E-3, failureProbability );
+            Assert.IsGreaterThan(5E-4, failureProbability, $"Probability too small: {failureProbability:E6}");
+            Assert.IsLessThan(5E-3, failureProbability, $"Probability too large: {failureProbability:E6}");
 
             // Standard error should be reasonable (less than 50% of estimate)
             double relativeError = vegas.StandardError / Math.Abs(failureProbability);
-            Assert.IsLessThan(0.5, relativeError);
+            Assert.IsLessThan(0.5, relativeError, $"Relative error too large: {relativeError:P1}");
         }
 
         /// <summary>
@@ -255,12 +255,12 @@ public void Test_PowerTransform_VeryRareEvent()
             var failureProbability = vegas.Result;
 
             // Should be in ballpark of 1e-6 to 1e-5
-            Assert.IsGreaterThan(1E-7, failureProbability);
-            Assert.IsLessThan(1E-4, failureProbability);
+            Assert.IsGreaterThan(1E-7, failureProbability, $"Probability too small: {failureProbability:E2}");
+            Assert.IsLessThan(1E-4,failureProbability, $"Probability too large: {failureProbability:E2}");
 
             // For very rare events, relative error can be higher but should be finite
             double relativeError = vegas.StandardError / Math.Abs(failureProbability);
-            Assert.IsLessThan(1.0, relativeError);
+            Assert.IsLessThan(1.0, relativeError, $"Relative error too large: {relativeError:P1}");
             Assert.IsFalse(double.IsNaN(failureProbability), "Result should not be NaN");
             Assert.IsFalse(double.IsInfinity(failureProbability), "Result should not be infinite");
         }
@@ -311,13 +311,13 @@ public void Test_PowerTransform_ProbabilityRange()
             vegas.Integrate();
 
             // Verify samples were generated
-            Assert.IsGreaterThan( 0, sampleCount );
+            Assert.IsGreaterThan(0,sampleCount , "No samples generated");
 
             // With γ=4, should see strong tail focus (many samples near 1.0)
-            Assert.IsGreaterThan(0.99, maxObserved );
+            Assert.IsGreaterThan(0.99,maxObserved, $"Max probability too low: {maxObserved}");
 
             // Should still have some diversity (not all at 1.0)
-            Assert.IsLessThan(0.5, minObserved );
+            Assert.IsLessThan(0.5,minObserved, $"Min probability too high: {minObserved}");
 
         }
 
diff --git a/Test_Numerics/Mathematics/Linear Algebra/Test_EigenValueDecomposition.cs b/Test_Numerics/Mathematics/Linear Algebra/Test_EigenValueDecomposition.cs
index d0dd8fb0..8bfc4c58 100644
--- a/Test_Numerics/Mathematics/Linear Algebra/Test_EigenValueDecomposition.cs	
+++ b/Test_Numerics/Mathematics/Linear Algebra/Test_EigenValueDecomposition.cs	
@@ -185,7 +185,7 @@ public void SymEig_3x3_RepeatedEigenvalues_AllTwos()
 
             // Max eigen residual
             var maxRes = MaxEigenResidual(A, V, w);
-            Assert.IsLessThan(1e-12, maxRes);
+            Assert.IsLessThan(1e-12, maxRes, $"Max eigen residual too large: {maxRes}");
         }
 
         /// <summary>
@@ -232,7 +232,7 @@ public void SymEig_8x8_TridiagonalToeplitz_KnownSpectrum()
 
             // Max eigen residual
             var maxRes = MaxEigenResidual(A, V, w);
-            Assert.IsLessThan(1e-8, maxRes);
+            Assert.IsLessThan(1e-8, maxRes, $"Max eigen residual too large: {maxRes}");
         }
 
         /// <summary>
@@ -268,7 +268,7 @@ public void SymEig_5x5_NearlyDiagonal_SmallCoupling()
 
             // Max eigen residual
             var maxRes = MaxEigenResidual(A, V, w);
-            Assert.IsLessThan(1e-8, maxRes);
+            Assert.IsLessThan(1e-8, maxRes, $"Max eigen residual too large: {maxRes}");
         }
 
         // ---------- Helpers ----------
diff --git a/Test_Numerics/Mathematics/Linear Algebra/Test_GaussJordanElimination.cs b/Test_Numerics/Mathematics/Linear Algebra/Test_GaussJordanElimination.cs
index 8359847e..1de9a5f5 100644
--- a/Test_Numerics/Mathematics/Linear Algebra/Test_GaussJordanElimination.cs	
+++ b/Test_Numerics/Mathematics/Linear Algebra/Test_GaussJordanElimination.cs	
@@ -59,7 +59,7 @@ public void Test_GaussJordanElim()
             for (int i = 0; i < A.NumberOfRows; i++)
             {
                 for (int j = 0; j < A.NumberOfColumns - 1; j++)
-                    Assert.AreEqual(A[i, j] == true_IA[i, j], true);
+                    Assert.AreEqual(A[i, j],true_IA[i, j]);
             }
 
             /// Recreated Gauss Jordan test in R to compare the inverted A matrices.
diff --git a/Test_Numerics/Mathematics/Special Functions/Test_Gamma.cs b/Test_Numerics/Mathematics/Special Functions/Test_Gamma.cs
index 953ceefb..8c67223d 100644
--- a/Test_Numerics/Mathematics/Special Functions/Test_Gamma.cs	
+++ b/Test_Numerics/Mathematics/Special Functions/Test_Gamma.cs	
@@ -68,7 +68,7 @@ public void Test_Function()
             for (int i = 0; i < testValid.Length; i++)
             {
                 testResults[i] = Gamma.Function(testX[i]);
-                Assert.IsLessThan(0.01, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
+                Assert.IsLessThan(0.01d, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
             }
         }
 
@@ -85,7 +85,7 @@ public void Test_Lanczos()
             for (int i = 0; i < testValid.Length; i++)
             {
                 testResults[i] = Gamma.Lanczos(testX[i]);
-                Assert.IsLessThan(0.01, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
+                Assert.IsLessThan(0.01d, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
             }
         }
 
@@ -157,7 +157,7 @@ public void Test_Trigamma()
             for (int i = 0; i < testValid.Length; i++)
             {
                 testResults[i] = Gamma.Trigamma(testX[i]);
-                Assert.IsLessThan(0.01, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
+                Assert.IsLessThan(0.01d,Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
             }
         }
 
@@ -187,7 +187,7 @@ public void Test_LogGamma()
             for (int i = 0; i < testValid.Length; i++)
             {
                 testResults[i] = Gamma.LogGamma(testX[i]);
-                Assert.IsLessThan(0.01, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
+                Assert.IsLessThan(0.01d,Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
             }
         }
 
@@ -227,7 +227,7 @@ public void Test_Incomplete()
             for (int i = 0; i < testValid.Length; i++)
             {
                 testResults[i] = Gamma.Incomplete(testX[i], testA[i]);
-                Assert.IsLessThan(0.01,Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
+                Assert.IsLessThan(0.01d, Math.Abs(testValid[i] - testResults[i]) / testValid[i]);
             }
 
         }
diff --git a/Test_Numerics/Mathematics/Special Functions/Test_SpecialFunctions.cs b/Test_Numerics/Mathematics/Special Functions/Test_SpecialFunctions.cs
index 30dd152e..be9786fe 100644
--- a/Test_Numerics/Mathematics/Special Functions/Test_SpecialFunctions.cs	
+++ b/Test_Numerics/Mathematics/Special Functions/Test_SpecialFunctions.cs	
@@ -209,7 +209,7 @@ public void Test_CombinationsNum()
 
             var ccLen = cc.Length;
             // Length of cc should be the possible number of combinations * the number of elements in each combination (5)
-            Assert.AreEqual(possible * 5, ccLen);
+            Assert.HasCount((int)possible * 5, cc);
 
             // How many of subsets of combinations there should be
             // For example, there are 5 ways to have only one #1 in the array, with the other 4 elements being #0
diff --git a/Test_Numerics/Sampling/Test_Stratification.cs b/Test_Numerics/Sampling/Test_Stratification.cs
index 8a6f04d4..7ce29f83 100644
--- a/Test_Numerics/Sampling/Test_Stratification.cs
+++ b/Test_Numerics/Sampling/Test_Stratification.cs
@@ -117,7 +117,7 @@ public void Test_XToProbability()
                 weights += probs[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d,weights,  1E-8);
         }
 
         /// <summary>
@@ -143,7 +143,7 @@ public void Test_XToExceedanceProbability()
                 weights += probs[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights,  1E-8);
 
         }
 
@@ -203,7 +203,7 @@ public void Test_Probabilities()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -225,7 +225,7 @@ public void Test_Probabilities_Multi()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -244,7 +244,7 @@ public void Test_Probabilities_Log10()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -264,7 +264,7 @@ public void Test_Probabilities_Normal()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -283,7 +283,7 @@ public void Test_ExceedanceProbabilities()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -305,7 +305,7 @@ public void Test_ExceedanceProbabilities_Multi()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -324,7 +324,7 @@ public void Test_ExceedanceProbabilities_Log10()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
 
         /// <summary>
@@ -343,7 +343,7 @@ public void Test_ExceedanceProbabilities_Normal()
                 weights += bins[i].Weight;
             }
             // Check weights sum to 1.0
-            Assert.AreEqual(weights, 1.0d, 1E-8);
+            Assert.AreEqual(1.0d, weights, 1E-8);
         }
     }
 }
diff --git a/Test_Numerics/Serialization/JsonConverterDemo.cs b/Test_Numerics/Serialization/JsonConverterDemo.cs
index 50e936c4..c98258fd 100644
--- a/Test_Numerics/Serialization/JsonConverterDemo.cs
+++ b/Test_Numerics/Serialization/JsonConverterDemo.cs
@@ -39,14 +39,14 @@ namespace Test_Numerics.Serialization
     /// Demonstration of custom JSON converters for complex types.
     /// </summary>
     [TestClass]
-    public static class JsonConverterDemo
+    public class JsonConverterDemo
     {
         /// <summary>
         /// Demonstrates how the custom converters handle 2D arrays and complex distribution objects.
         /// </summary>
         /// 
         [TestMethod]
-        public static void RunDemo()
+        public void RunDemo()
         {
             Console.WriteLine("=== JSON Converter Demo ===\n");
 
diff --git a/Test_Numerics/Serialization/Test_JsonSerialization.cs b/Test_Numerics/Serialization/Test_JsonSerialization.cs
index 51c25d9b..6ec44ec2 100644
--- a/Test_Numerics/Serialization/Test_JsonSerialization.cs
+++ b/Test_Numerics/Serialization/Test_JsonSerialization.cs
@@ -99,14 +99,16 @@ public void Test_UncertaintyAnalysisResults_ArraySerialization()
 
             // Assert
             Assert.IsNotNull(deserialized.ModeCurve);
-            Assert.HasCount(original.ModeCurve.Length, deserialized.ModeCurve);
+            var modeCurveLen = original.ModeCurve.Length;
+            Assert.HasCount(modeCurveLen, deserialized.ModeCurve);
             for (int i = 0; i < original.ModeCurve.Length; i++)
             {
                 Assert.AreEqual(original.ModeCurve[i], deserialized.ModeCurve[i], 1e-10);
             }
 
             Assert.IsNotNull(deserialized.MeanCurve);
-            Assert.HasCount(original.MeanCurve.Length, deserialized.MeanCurve);
+            var meanCurveLen = original.MeanCurve.Length;
+            Assert.HasCount(meanCurveLen, deserialized.MeanCurve);
             for (int i = 0; i < original.MeanCurve.Length; i++)
             {
                 Assert.AreEqual(original.MeanCurve[i], deserialized.MeanCurve[i], 1e-10);
@@ -165,7 +167,8 @@ public void Test_UncertaintyAnalysisResults_ParameterSetsSerialization()
             var desLen = deserialized.ParameterSets.Length;
             // Assert
             Assert.IsNotNull(deserialized.ParameterSets);
-            Assert.AreEqual(origLen, desLen);
+            var parameterSetsLen = original.ParameterSets.Length;
+            Assert.HasCount(parameterSetsLen, deserialized.ParameterSets);
 
             for (int i = 0; i < original.ParameterSets.Length; i++)
             {
@@ -175,7 +178,8 @@ public void Test_UncertaintyAnalysisResults_ParameterSetsSerialization()
                 if (original.ParameterSets[i].Values != null)
                 {
                     Assert.IsNotNull(deserialized.ParameterSets[i].Values);
-                    Assert.HasCount(original.ParameterSets[i].Values.Length, deserialized.ParameterSets[i].Values);
+                    var parameterValuesLen = original.ParameterSets[i].Values.Length;
+                    Assert.HasCount(parameterValuesLen, deserialized.ParameterSets[i].Values);
 
                     for (int j = 0; j < original.ParameterSets[i].Values.Length; j++)
                     {
@@ -248,11 +252,11 @@ public void Test_UncertaintyAnalysisResults_EmptyArrays()
             // Assert
             Assert.IsNotNull(deserialized);
             Assert.IsNotNull(deserialized.ParameterSets);
-            Assert.IsEmpty( deserialized.ParameterSets);
+            Assert.HasCount(0, deserialized.ParameterSets);
             Assert.IsNotNull(deserialized.ModeCurve);
-            Assert.IsEmpty(deserialized.ModeCurve);
+            Assert.HasCount(0, deserialized.ModeCurve);
             Assert.IsNotNull(deserialized.MeanCurve);
-            Assert.IsEmpty(deserialized.MeanCurve);
+            Assert.HasCount(0, deserialized.MeanCurve);
         }
 
         /// <summary>
@@ -300,7 +304,8 @@ public void Test_MCMCResults_BasicSerialization()
             // Assert
             Assert.IsNotNull(deserialized);
             Assert.IsNotNull(deserialized.AcceptanceRates);
-            Assert.AreEqual(origLen, desLen);
+            var acceptanceRatesLen = original.AcceptanceRates.Length;
+            Assert.HasCount(acceptanceRatesLen, deserialized.AcceptanceRates);
 
             for (int i = 0; i < original.AcceptanceRates.Length; i++)
             {
@@ -331,12 +336,14 @@ public void Test_MCMCResults_MarkovChainsSerialization()
             var desLen = deserialized.MarkovChains.Length;
             // Assert
             Assert.IsNotNull(deserialized.MarkovChains);
-            Assert.AreEqual(origLen, desLen);
+            var markovChainsLen = original.MarkovChains.Length;
+            Assert.HasCount(markovChainsLen, deserialized.MarkovChains);
 
             for (int i = 0; i < original.MarkovChains.Length; i++)
             {
                 Assert.IsNotNull(deserialized.MarkovChains[i]);
-                Assert.HasCount(original.MarkovChains[i].Count, deserialized.MarkovChains[i]);
+                var chainCount = original.MarkovChains[i].Count;
+                Assert.HasCount(chainCount, deserialized.MarkovChains[i]);
 
                 for (int j = 0; j < original.MarkovChains[i].Count; j++)
                 {
@@ -374,7 +381,8 @@ public void Test_MCMCResults_OutputSerialization()
 
             // Assert
             Assert.IsNotNull(deserialized.Output);
-            Assert.HasCount(original.Output.Count, deserialized.Output);
+            var outputCount = original.Output.Count;
+            Assert.HasCount(outputCount, deserialized.Output);
 
             for (int i = 0; i < original.Output.Count; i++)
             {
@@ -429,7 +437,8 @@ public void Test_MCMCResults_MeanLogLikelihoodSerialization()
 
             // Assert
             Assert.IsNotNull(deserialized.MeanLogLikelihood);
-            Assert.HasCount(original.MeanLogLikelihood.Count, deserialized.MeanLogLikelihood);
+            var meanLogLikelihoodCount = original.MeanLogLikelihood.Count;
+            Assert.HasCount(meanLogLikelihoodCount, deserialized.MeanLogLikelihood);
 
             for (int i = 0; i < original.MeanLogLikelihood.Count; i++)
             {
@@ -496,7 +505,8 @@ public void Test_MCMCResults_LargeDataSet()
             var deserializedLen = deserialized.MarkovChains.Length;
             // Assert
             Assert.IsNotNull(deserialized);
-            Assert.AreEqual(origLen, deserializedLen);
+            var origChainsLen = original.MarkovChains.Length;
+            Assert.HasCount(origChainsLen, deserialized.MarkovChains);
 
             // Verify first and last elements to ensure proper serialization
             var firstOriginal = original.MarkovChains[0][0];
@@ -539,15 +549,15 @@ public void Test_JsonSerializerOptions_Configuration()
 
             // Assert
             // Verify that WriteIndented is false (no formatting whitespace)
-            Assert.DoesNotContain(jsonString,("\n"));
-            Assert.DoesNotContain(jsonString, ("  ")); // No indentation
+            Assert.DoesNotContain("\n",jsonString);
+            Assert.DoesNotContain("  ",jsonString); // No indentation
 
             // Verify that null values are not included (DefaultIgnoreCondition)
-            Assert.DoesNotContain(jsonString,  ("\"ParentDistribution\":null"));
+            Assert.DoesNotContain("\"ParentDistribution\":null", jsonString);
 
             // Verify that fields are included (IncludeFields = true)
-            Assert.DoesNotContain(jsonString, ("\"AIC\":"));
-            Assert.DoesNotContain(jsonString, ("\"BIC\":"));
+            Assert.Contains("\"AIC\":",jsonString);
+            Assert.Contains("\"BIC\":",jsonString);
         }
 
         #endregion