export normalmix.Rd

DESCRIPTION

@@ -13,8 +13,8 @@ Authors@R: c(person("Matthew","Stephens",role="aut",
              person("Jason","Willwerscheid",role="aut"),
              person("Nan","Xiao",role="aut"),
              person("Mazon","Zeng",role="ctb"))
-Version: 2.2-47
-Date: 2020-02-19
+Version: 2.2-48
+Date: 2020-04-06
 Title: Methods for Adaptive Shrinkage, using Empirical Bayes
 Description: The R package 'ashr' implements an Empirical Bayes
     approach for large-scale hypothesis testing and false discovery
@@ -53,5 +53,4 @@ NeedsCompilation: yes
 LazyData: true
 URL: https://github.com/stephens999/ashr
 BugReports: https://github.com/stephens999/ashr/issues
-RoxygenNote: 7.0.2
 VignetteBuilder: knitr

NAMESPACE

@@ -126,6 +126,7 @@ export(postsd)
 export(prune)
 export(qval.from.lfdr)
 export(set_data)
+export(tnormalmix)
 export(unimix)
 export(vcdf_post)
 export(w_mixEM)

R/tnormalmix.R

@@ -1,3 +1,22 @@
+#' @title Constructor for tnormalmix class
+#'
+#' @description Creates an object of class tnormalmix (finite mixture
+#'     of truncated univariate normals)
+#'
+#' @details None
+#'
+#' @param pi vector of mixture proportions (length k say)
+#' @param mean vector of means (length k)
+#' @param sd vector of standard deviations (length k)
+#' @param a vector of left truncation points of each component (length k)
+#' @param b vector of right truncation points of each component (length k)
+#'
+#' @return an object of class tnormalmix
+#'
+#' @export
+#'
+#' @examples tnormalmix(c(0.5,0.5),c(0,0),c(1,2),c(-10,0),c(0,10))
+#'
 tnormalmix = function(pi,mean,sd,a,b){
   structure(data.frame(pi,mean,sd,a,b),class="tnormalmix")
 }