X-Git-Url: https://git.nmode.ca/Rnaught/blobdiff_plain/14ec98c9bfc49f963e30159dc7db9f554088fb44..a50ca5855eecf12908327252d627df3af076fc88:/R/WP_known.R

diff --git a/R/WP_known.R b/R/WP_known.R
index 40f1ded..4c43ed2 100644
--- 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)
+}