diff --git a/R/RcppExports.R b/R/RcppExports.R
index 2a320be..022bb7b 100644
--- a/R/RcppExports.R
+++ b/R/RcppExports.R
@@ -24,6 +24,10 @@ cossimVec <- function(x, y, narm) {
 #' \item{5 (AMEAN RULE) Arithmetic mean of length of intersect divided by length of \emph{both} seeds no less than \code{multiLinkageThr}, a few items tend to appear in multiple clusters.}
 #' }
 #' 
+#' @return A list of integer vectors. Each element represents a cluster
+#' and contains the indices of rows belonging to that cluster. Rows can
+#' appear in multiple clusters (fuzzy clustering).
+#'
 #' @author Jitao David Zhang <jitao_david.zhang@roche.com>
 #'
 #' @note
diff --git a/R/davidClustering_kappa_R.R b/R/davidClustering_kappa_R.R
index 9b048b6..1369b17 100644
--- a/R/davidClustering_kappa_R.R
+++ b/R/davidClustering_kappa_R.R
@@ -19,18 +19,6 @@
 #' biological module-centric algorithm to functionally analyze large gene
 #' lists. Genome Biology, 2007. \doi{10.1186/gb-2007-8-9-r183}
 #'
-#' @examples 
-#' synData <- matrix(c(rep(c(rep(1, 10), rep(0, 5)), 3),
-#' rep(0, 4), rep(1, 7), rep(0,4),
-#' rep(c(rep(0,5), rep(1,10)), 3),
-#' rep(c(rep(0,3), 1), 4)[-16]), ncol=15, byrow=TRUE)
-#' rownames(synData) <- sprintf("Gene %s", letters[1:8])
-#' colnames(synData) <- sprintf("t%d", 1:15)
-#' synRowKappa <- rowKappa(synData)
-#' synColKappa <- colKappa(synData)
-#' ribiosMath:::davidClustering_kappa_R(synRowKappa, removeRedundant = TRUE, debug=FALSE)
-#' ribiosMath:::davidClustering_kappa_R(synColKappa, removeRedundant = TRUE, debug=FALSE)
-#' 
 davidClustering_kappa_R <- function(kappaMatrix, 
                             kappaThr = 0.35,
                             initialGroupMembership=3,
diff --git a/R/kappa.R b/R/kappa.R
index f0ce61f..cbba02d 100644
--- a/R/kappa.R
+++ b/R/kappa.R
@@ -9,9 +9,6 @@
 #' @family kappa functions
 #' @seealso \code{\link{colKappa}} to calculate the same statistic using a linear algebra based routine
 #' 
-#' @examples
-#' testMat <- cbind(c(1,1,0,0,1,0), c(1,1,0,1,1,0))
-#' ribiosMath:::kappaSimp(testMat)
 kappaSimp <- function(matrix, minOverlap=0) {
     res <- .Call('_ribiosMath_colKappaSimp', matrix, minOverlap)
     return(res)
diff --git a/R/pca.R b/R/pca.R
index c10f2b3..e8b9742 100644
--- a/R/pca.R
+++ b/R/pca.R
@@ -115,9 +115,11 @@ biplot.bidata <- function(bidata,
   } else {
     points(x, cex=cex[1L], col=col[1L],...)
   }
+  oldpar <- par(no.readonly = TRUE)
+  on.exit(par(oldpar))
   par(new = TRUE)
   dev.hold()
-  on.exit(dev.flush())
+  on.exit(dev.flush(), add = TRUE)
 
   plot(y, axes = FALSE, type = "n", xlim = xlim * ratio, ylim = ylim * 
        ratio, xlab = "", ylab = "", col = col[1L], ...)
diff --git a/R/simulate_from_density.R b/R/simulate_from_density.R
index 860bd6d..2c67132 100644
--- a/R/simulate_from_density.R
+++ b/R/simulate_from_density.R
@@ -5,6 +5,8 @@
 #' can be aproximated using a gaussian kernel.
 #' @param vec Numeric vector
 #' @param N Integer, number of simulated instances
+#' @return A numeric vector of length \code{N} with values simulated from the
+#' kernel density estimate of \code{vec}.
 #' @author Iakov Davydov
 #' @examples
 #' my_vec <- c(23, 27, 26, 24, 25)
diff --git a/R/tfidf.R b/R/tfidf.R
index f0e9264..0e0f287 100644
--- a/R/tfidf.R
+++ b/R/tfidf.R
@@ -4,6 +4,9 @@
 #' @param idfVariant Variant of inverse document frequency. See details below.
 #' @param idfAddOne Logical, whether one should be added to both numerator and denominator to calculate IDF. See details below.
 #' 
+#' @return A numeric matrix of the same dimensions as \code{tdMat}, containing
+#' the TF-IDF values.
+#'
 #' @details
 #' \code{tfVariant} accepts following options:
 #' \describe{
diff --git a/man/davidClustering_kappa.Rd b/man/davidClustering_kappa.Rd
index 7947a5a..ea361ba 100644
--- a/man/davidClustering_kappa.Rd
+++ b/man/davidClustering_kappa.Rd
@@ -32,6 +32,11 @@ Currently following merge rules are implemented:
 \item{5 (AMEAN RULE) Arithmetic mean of length of intersect divided by length of \emph{both} seeds no less than \code{multiLinkageThr}, a few items tend to appear in multiple clusters.}
 }}
 }
+\value{
+A list of integer vectors. Each element represents a cluster
+and contains the indices of rows belonging to that cluster. Rows can
+appear in multiple clusters (fuzzy clustering).
+}
 \description{
 The function implements the Hierarhical fuzzy multi-linkage partitioning method used in the DAVID Bioinformatics tool.
 }
diff --git a/man/davidClustering_kappa_R.Rd b/man/davidClustering_kappa_R.Rd
index 61fc6cb..b61a4cd 100644
--- a/man/davidClustering_kappa_R.Rd
+++ b/man/davidClustering_kappa_R.Rd
@@ -31,19 +31,6 @@ The function implements the Hierarhical fuzzy multi-linkage partitioning method
 }
 \note{
 The function has only been tested in a few anecdotal examples. Cautions and more systematic tests are required before it is applied to critical datasets.
-}
-\examples{
-synData <- matrix(c(rep(c(rep(1, 10), rep(0, 5)), 3),
-rep(0, 4), rep(1, 7), rep(0,4),
-rep(c(rep(0,5), rep(1,10)), 3),
-rep(c(rep(0,3), 1), 4)[-16]), ncol=15, byrow=TRUE)
-rownames(synData) <- sprintf("Gene \%s", letters[1:8])
-colnames(synData) <- sprintf("t\%d", 1:15)
-synRowKappa <- rowKappa(synData)
-synColKappa <- colKappa(synData)
-ribiosMath:::davidClustering_kappa_R(synRowKappa, removeRedundant = TRUE, debug=FALSE)
-ribiosMath:::davidClustering_kappa_R(synColKappa, removeRedundant = TRUE, debug=FALSE)
-
 }
 \references{
 Huang et al. The DAVID Gene Functional Classification Tool: a novel
diff --git a/man/kappaSimp.Rd b/man/kappaSimp.Rd
index 14cca93..83b7f96 100644
--- a/man/kappaSimp.Rd
+++ b/man/kappaSimp.Rd
@@ -17,10 +17,6 @@ A matrix of size nxn if the input matrix is of size mxn (m is arbitrary)
 \description{
 Calculate column-wise kappa statistics of a matrix, using a simple procedure by going through the matrix and counting
 }
-\examples{
-testMat <- cbind(c(1,1,0,0,1,0), c(1,1,0,1,1,0))
-ribiosMath:::kappaSimp(testMat)
-}
 \seealso{
 \code{\link{colKappa}} to calculate the same statistic using a linear algebra based routine
 
diff --git a/man/simulate_from_density.Rd b/man/simulate_from_density.Rd
index 6606ba2..5d56e5c 100644
--- a/man/simulate_from_density.Rd
+++ b/man/simulate_from_density.Rd
@@ -11,6 +11,10 @@ simulate_from_density(vec, N = 1e+05)
 
 \item{N}{Integer, number of simulated instances}
 }
+\value{
+A numeric vector of length \code{N} with values simulated from the
+kernel density estimate of \code{vec}.
+}
 \description{
 Compared with bootstrapping, the results do not reveal input values, and the
 empirical distribution can be smoother. The function assumes that the distribution
diff --git a/man/tfidf.Rd b/man/tfidf.Rd
index 5173cf7..3f7f76c 100644
--- a/man/tfidf.Rd
+++ b/man/tfidf.Rd
@@ -20,6 +20,10 @@ tfidf(
 
 \item{idfAddOne}{Logical, whether one should be added to both numerator and denominator to calculate IDF. See details below.}
 }
+\value{
+A numeric matrix of the same dimensions as \code{tdMat}, containing
+the TF-IDF values.
+}
 \description{
 Calculate TF-IDF using a input matrix with terms in rows and documents in columns
 }