#' posterior mean).
#'
#' @references [Bettencourt and Riberio (PloS One, 2008)](
-#' https://doi.org/10.1371/journal.pone.0002185)
+#' https://doi.org/10.1371/journal.pone.0002185)
#'
#' @seealso `vignette("seq_bayes_post", package = "Rnaught")` for examples of
#' using the posterior distribution.
#' # Note that the following always holds:
#' estimate == sum(posterior$supp * posterior$pmf)
seq_bayes <- function(cases, mu, kappa = 20, post = FALSE) {
- if (any(cases == 0)) {
- times <- which(cases > 0)
- if (length(times) < 2) {
- stop("Vector of case counts must contain at least two positive integers.")
- }
- cases <- cases[times]
- } else {
- times <- seq_along(cases)
+ validate_cases(cases, min_length = 2, min_count = 0)
+ if (!is_real(mu) || mu <= 0) {
+ stop("The serial interval (`mu`) must be a number greater than 0.",
+ call. = FALSE
+ )
+ }
+ if (!is_real(kappa) || kappa < 1) {
+ stop(
+ paste("The largest value of the uniform prior (`kappa`)",
+ "must be a number greater than or equal to 1."
+ ), call. = FALSE
+ )
+ }
+ if (!identical(post, TRUE) && !identical(post, FALSE)) {
+ stop("The posterior flag (`post`) must be set to `TRUE` or `FALSE`.",
+ call. = FALSE
+ )
+ }
+
+ times <- which(cases > 0)
+ if (length(times) < 2) {
+ stop("Case counts must contain at least two positive integers.",
+ call. = FALSE
+ )
}
+ cases <- cases[times]
support <- seq(0, kappa, 0.01)
tau <- diff(times)