Normalize line endings

2 files changed, 106 insertions, 106 deletions

diff --git a/R/ID.R b/R/ID.R
index cd991a4..0e3cc35 100644
--- a/R/ID.R
+++ b/R/ID.R
@@ -1,48 +1,48 @@
-#' 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.

-#'

-#' 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.

-#'

-#' @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.

-#'

-#' @return \code{ID} returns a single value, the estimate of R0.

-#'

-#' @examples

-#' ## ===================================================== ##

-#' ## Illustrate on 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      ##

-#' ## ========================================================= ##

-#'

-#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days

-#'

-#' @export

-ID <- function(NT, mu) {

-    NT <- as.numeric(NT)

-    TT <- length(NT)

-    s <- (1:TT) / mu

-    y <- log(NT) / s

-

-    R0_ID <- exp(sum(y) / TT)

-

-    return(R0_ID)

-}

+#' 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.
+#'
+#' 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.
+#'
+#' @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.
+#'
+#' @return \code{ID} returns a single value, the estimate of R0.
+#'
+#' @examples
+#' ## ===================================================== ##
+#' ## Illustrate on 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      ##
+#' ## ========================================================= ##
+#'
+#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
+#'
+#' @export
+ID <- function(NT, mu) {
+    NT <- as.numeric(NT)
+    TT <- length(NT)
+    s <- (1:TT) / mu
+    y <- log(NT) / s
+
+    R0_ID <- exp(sum(y) / TT)
+
+    return(R0_ID)
+}
diff --git a/R/IDEA.R b/R/IDEA.R
index 20a9401..854acd7 100644
--- a/R/IDEA.R
+++ b/R/IDEA.R
@@ -1,58 +1,58 @@
-#' IDEA 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 method is closely related to that implemented in \code{ID}. The method is based on an 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.

-#'

-#' @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.

-#'

-#' @return \code{IDEA} returns a single value, the estimate of R0.

-#'

-#' @examples

-#' ## ===================================================== ##

-#' ## Illustrate on 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

-#' IDEA(NT=NT, mu=5/7)

-#' ## obtain Rhat when serial distribution has mean of three days

-#' IDEA(NT=NT, mu=3/7)

-#'

-#' ## ========================================================= ##

-#' ## Compute Rhat using only the first five weeks of data      ##

-#' ## ========================================================= ##

-#'

-#' IDEA(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days

-#'

-#' @export

-IDEA <- function(NT, mu) {

-    if (length(NT) < 2)

-        print("Warning: length of NT should be at least two.")

-    else {

-        NT <- as.numeric(NT)

-        TT <- length(NT)

-        s <- (1:TT) / mu

-

-        y1 <- log(NT) / s

-        y2 <- s^2

-        y3 <- log(NT)

-

-        IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3)

-        IDEA2 <- TT * sum(y2) - sum(s)^2

-        IDEA <- exp(IDEA1 / IDEA2)

-

-        return(IDEA)

-    }

-}

+#' IDEA 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 method is closely related to that implemented in \code{ID}. The method is based on an 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.
+#'
+#' @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.
+#'
+#' @return \code{IDEA} returns a single value, the estimate of R0.
+#'
+#' @examples
+#' ## ===================================================== ##
+#' ## Illustrate on 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
+#' IDEA(NT=NT, mu=5/7)
+#' ## obtain Rhat when serial distribution has mean of three days
+#' IDEA(NT=NT, mu=3/7)
+#'
+#' ## ========================================================= ##
+#' ## Compute Rhat using only the first five weeks of data      ##
+#' ## ========================================================= ##
+#'
+#' IDEA(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
+#'
+#' @export
+IDEA <- function(NT, mu) {
+    if (length(NT) < 2)
+        print("Warning: length of NT should be at least two.")
+    else {
+        NT <- as.numeric(NT)
+        TT <- length(NT)
+        s <- (1:TT) / mu
+
+        y1 <- log(NT) / s
+        y2 <- s^2
+        y3 <- log(NT)
+
+        IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3)
+        IDEA2 <- TT * sum(y2) - sum(s)^2
+        IDEA <- exp(IDEA1 / IDEA2)
+
+        return(IDEA)
+    }
+}