Rename ID and IDEA

1 files changed, 0 insertions, 46 deletions

diff --git a/R/ID.R b/R/ID.R
deleted file mode 100644
index 7e8a04d..0000000
--- a/R/ID.R
+++ /dev/null
@@ -1,46 +0,0 @@
-#' 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
-#' # Weekly data:
-#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
-#'
-#' # Obtain R0 when the serial distribution has a mean of five days.
-#' ID(NT, mu = 5 / 7)
-#'
-#' # 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
-
-  R0_ID <- exp(sum(y) / TT)
-
-  return(R0_ID)
-}