From b87620843bcae4fc1cb6a9e9caaf52944e93b7b7 Mon Sep 17 00:00:00 2001
From: Naeem Model <me@nmode.ca>
Date: Fri, 30 Jun 2023 00:04:19 +0000
Subject: Re-gen docs and prevent genning of internal functions

---
 R/WP.R          | 4 ++--
 R/WP_internal.R | 8 ++++----
 R/seqB.R        | 4 ++--
 3 files changed, 8 insertions(+), 8 deletions(-)

(limited to 'R')

diff --git a/R/WP.R b/R/WP.R
index a6e5354..5440e35 100644
--- a/R/WP.R
+++ b/R/WP.R
@@ -43,8 +43,8 @@
 #'               \code{scale.max}, which is the largest possible value of the
 #'               scale parameter; and the third is \code{shape.max}, which is
 #'               the largest possible value of the shape parameter. Defaults to
-#'               \code{B=100, scale.max=10, shape.max=10}. For both shape and
-#'               scale, the smallest possible value is 1/\code{B}.
+#'               \code{B = 100, scale.max = 10, shape.max = 10}. For both shape
+#'               and scale, the smallest possible value is 1/\code{B}.
 #' @param tol Cutoff value for cumulative distribution function of the
 #'            pre-discretization gamma serial distribution. Defaults to 0.999
 #'            (i.e. in the discretization, the maximum is chosen such that the
diff --git a/R/WP_internal.R b/R/WP_internal.R
index 420d0c0..dd10d29 100644
--- a/R/WP_internal.R
+++ b/R/WP_internal.R
@@ -9,7 +9,7 @@
 #'
 #' @return The function returns the maximum likelihood estimator of R0.
 #'
-#' @keywords internal
+#' @noRd
 WP_known <- function(NT, p) {
   k <- length(p)
   TT <- length(NT) - 1
@@ -54,7 +54,7 @@ WP_known <- function(NT, p) {
 #'
 #' @importFrom stats pgamma qgamma
 #'
-#' @keywords internal
+#' @noRd
 WP_unknown <- function(NT, B = 100, shape.max = 10, scale.max = 10,
                        tol = 0.999) {
   shape <- seq(0, shape.max, length.out = B + 1)
@@ -91,14 +91,14 @@ WP_unknown <- function(NT, B = 100, shape.max = 10, scale.max = 10,
 #' This is a background/internal function called by \code{WP}. It computes the
 #' log-likelihood.
 #'
-#' @param NT Vector of case counts.
 #' @param p Discretized version of the serial distribution.
+#' @param NT Vector of case counts.
 #' @param R0 Basic reproductive ratio.
 #'
 #' @return This function returns the log-likelihood at the input variables and
 #'         parameters.
 #'
-#' @keywords internal
+#' @noRd
 computeLL <- function(p, NT, R0) {
   k <- length(p)
   TT <- length(NT) - 1
diff --git a/R/seqB.R b/R/seqB.R
index e51117f..09efe07 100644
--- a/R/seqB.R
+++ b/R/seqB.R
@@ -11,7 +11,7 @@
 #' posterior distribution. The prior distribution is the initial belief of the
 #' distribution of R0, which is the uninformative uniform distribution with
 #' values between zero and \code{kappa}. Users can change the value of
-#' /code{kappa} only (i.e., the prior distribution cannot be changed from the
+#' \code{kappa} only (i.e., the prior distribution cannot be changed from the
 #' uniform). As more case counts are observed, the influence of the prior
 #' distribution should lessen on the final estimate \code{Rhat}.
 #'
@@ -41,7 +41,7 @@
 #'         \code{Rhat} is the estimate of R0 (the posterior mean),
 #'         \code{posterior} is the posterior distribution of R0 from which
 #'         alternate estimates can be obtained (see examples), and \code{group}
-#'         is an indicator variable (if \code{group=TRUE}, zero values of NT
+#'         is an indicator variable (if \code{group == TRUE}, zero values of NT
 #'         were input and grouping was done to obtain \code{Rhat}). The variable
 #'         \code{posterior} is returned as a list made up of \code{supp} (the
 #'         support of the distribution) and \code{pmf} (the probability mass
-- 
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