From 80df3ed7a280f86a3b9b5443309487d428f4fe95 Mon Sep 17 00:00:00 2001
From: Naeem Model <me@nmode.ca>
Date: Thu, 29 Jun 2023 23:47:01 +0000
Subject: Re-style code and enforce 80 character line limit

---
 R/ID.R | 56 +++++++++++++++++++++++++++-----------------------------
 1 file changed, 27 insertions(+), 29 deletions(-)

(limited to 'R/ID.R')

diff --git a/R/ID.R b/R/ID.R
index 0e3cc35..7e8a04d 100644
--- a/R/ID.R
+++ b/R/ID.R
@@ -1,48 +1,46 @@
 #' ID method
 #'
-#' This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013).
-#' See details for implementation notes.
+#' This function implements a least squares estimation method of R0 due to
+#' Fisman et al. (PloS One, 2013). See details for implementation notes.
 #'
-#' The method is based on a straightforward incidence decay model. The estimate of R0 is the value which
-#' minimizes the sum of squares between observed case counts and cases counts 'expected' under the model.
+#' The method is based on a straightforward incidence decay model. The estimate
+#' of R0 is the value which minimizes the sum of squares between observed case
+#' counts and cases counts 'expected' under the model.
 #'
-#' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.
-#' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.
-#' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.
-#' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.
+#' This method is based on an approximation of the SIR model, which is most
+#' valid at the beginning of an epidemic. The method assumes that the mean of
+#' the serial distribution (sometimes called the serial interval) is known. The
+#' final estimate can be quite sensitive to this value, so sensitivity testing
+#' is strongly recommended. Users should be careful about units of time (e.g.,
+#' are counts observed daily or weekly?) when implementing.
 #'
 #' @param NT Vector of case counts.
-#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts
-#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one If case
-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.
+#' @param mu Mean of the serial distribution. This needs to match case counts
+#'           in time units. For example, if case counts are weekly and the
+#'           serial distribution has a mean of seven days, then \code{mu} should
+#'           be set to one. If case counts are daily and the serial distribution
+#'           has a mean of seven days, then \code{mu} should be set to seven.
 #'
 #' @return \code{ID} returns a single value, the estimate of R0.
 #'
 #' @examples
-#' ## ===================================================== ##
-#' ## Illustrate on weekly data                             ##
-#' ## ===================================================== ##
-#'
+#' # Weekly data:
 #' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
-#' ## obtain Rhat when serial distribution has mean of five days
-#' ID(NT=NT, mu=5/7)
-#' ## obtain Rhat when serial distribution has mean of three days
-#' ID(NT=NT, mu=3/7)
 #'
-#' ## ========================================================= ##
-#' ## Compute Rhat using only the first five weeks of data      ##
-#' ## ========================================================= ##
+#' # Obtain R0 when the serial distribution has a mean of five days.
+#' ID(NT, mu = 5 / 7)
 #'
-#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' ID(NT, mu = 3 / 7)
 #'
 #' @export
 ID <- function(NT, mu) {
-    NT <- as.numeric(NT)
-    TT <- length(NT)
-    s <- (1:TT) / mu
-    y <- log(NT) / s
+  NT <- as.numeric(NT)
+  TT <- length(NT)
+  s <- (1:TT) / mu
+  y <- log(NT) / s
 
-    R0_ID <- exp(sum(y) / TT)
+  R0_ID <- exp(sum(y) / TT)
 
-    return(R0_ID)
+  return(R0_ID)
 }
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