Ref to kummerM function. Examples: donttest

R/EM.R

@@ -22,7 +22,7 @@ EM <- function(z, eps = 1e-06) {
   #' with two attributes \code{attr(, "epsilon")} (precision of the result) and \code{attr(, "k")} (number of iterations).
   #'
   #' @details The  parameters \eqn{\beta} are computed using the EM algorithm
-  #' as presented in El Ghaziri et al.
+  #' as presented in El Ghaziri et al. The computation uses the \code{\link{kummerM}} function.
   #' This uses an iterative algorithm.
   #'
   #' The precision for the estimation of the parameters is given by the \code{eps} parameter.
@@ -37,6 +37,7 @@ EM <- function(z, eps = 1e-06) {
   #' @seealso \code{\link{dz}}: probability density of a Gaussian ratio.
   #' 
   #' @examples
+  #' \donttest{
   #' # First example
   #' beta1 <- 0.15
   #' rho1 <- 5.75
@@ -56,6 +57,7 @@ EM <- function(z, eps = 1e-06) {
   #' z2 <- rz(800, bet = beta2, rho = rho2, delta = delta2)
   #' 
   #' EM(z2)
+  #' }
   #' 
   #' @importFrom graphics plot
   #' @export

man/EM.Rd

@@ -30,12 +30,13 @@ using the EM (estimation-maximization) algorithm.
 }
 \details{
 The  parameters \eqn{\beta} are computed using the EM algorithm
-as presented in El Ghaziri et al.
+as presented in El Ghaziri et al. The computation uses the \code{\link{kummerM}} function.
 This uses an iterative algorithm.
 
 The precision for the estimation of the parameters is given by the \code{eps} parameter.
 }
 \examples{
+\donttest{
 # First example
 beta1 <- 0.15
 rho1 <- 5.75
@@ -55,6 +56,7 @@ set.seed(1234)
 z2 <- rz(800, bet = beta2, rho = rho2, delta = delta2)
 
 EM(z2)
+}
 
 }
 \references{