Changes performed before submission to CRAN

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

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,

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)

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], ...)

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)

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{