Remove 'shinyjs' dependency

[Rnaught] / R / WP_known.R

diff --git a/R/WP_known.R b/R/WP_known.R
index 40f1ded5907b7de61987ef7a19cec0e1832ee63c..4c43ed2dc1eb76ec3b72031ff22a77c0587d0415 100644 (file)
--- a/R/WP_known.R
+++ b/R/WP_known.R
@@ -1,27 +1,24 @@
 #' WP method background function WP_known
 #'
-#' This is a background/internal function called by \code{WP}.  It computes the maximum likelihood estimator of R0 assuming that the serial distribution is known and finite discrete.
+#' This is a background/internal function called by \code{WP}. It computes the maximum
+#' likelihood estimator of R0 assuming that the serial distribution is known and finite discrete.
 #'
-#' @param NT vector of case counts
-#' @param p discretized version of the serial distribution
-#' @return The function returns \code{Rhat}, the maximum likelihood estimator of R0.
-#' @export
-# 
-
-WP_known               <-      function(NT, p){
-       
-       k               <-      length(p)
-       TT              <-      length(NT)-1
-       mu_t    <-      rep(0, TT)
-       for(i in 1:TT){
-               Nt              <-      NT[i:max(1,i-k+1)]
-#              print(Nt)
-#              print(p[1:min(k,i)])
-               mu_t[i] <-      sum(p[1:min(k,i)]*Nt)   
-       }
-       Rhat    <-      sum(NT[-1])/sum(mu_t)
-       return(list(Rhat=Rhat))
-               
-}
+#' @param NT Vector of case counts.
+#' @param p Discretized version of the serial distribution.
+#'
+#' @return The function returns the maximum likelihood estimator of R0.
+#'
+#' @keywords internal
+WP_known <- function(NT, p) {
+    k <- length(p)
+    TT <- length(NT) - 1
+       mu_t <- rep(0, TT)
 
+    for (i in 1:TT) {
+        Nt <- NT[i:max(1, i-k+1)]
+        mu_t[i]        <- sum(p[1:min(k, i)] * Nt)
+    }
 
+    Rhat <- sum(NT[-1]) / sum(mu_t)
+       return(Rhat)
+}