Exported gpdfit, removed qgpd

Author: VisruthSK

NAMESPACE

@@ -106,6 +106,7 @@ export(example_loglik_array)
 export(example_loglik_matrix)
 export(extract_log_lik)
 export(find_model_names)
+export(gpdfit)
 export(is.kfold)
 export(is.loo)
 export(is.psis)

R/gpdfit.R

@@ -0,0 +1,38 @@
+#' Estimate parameters of the Generalized Pareto distribution
+#'
+#' Given a sample \eqn{x}, Estimate the parameters \eqn{k} and \eqn{\sigma} of
+#' the generalized Pareto distribution (GPD), assuming the location parameter is
+#' 0. By default the fit uses a prior for \eqn{k}, which will stabilize
+#' estimates for very small sample sizes (and low effective sample sizes in the
+#' case of MCMC samples). The weakly informative prior is a Gaussian prior
+#' centered at 0.5.
+#'
+#' @export
+#' @param x A numeric vector. The sample from which to estimate the parameters.
+#' @param wip Logical indicating whether to adjust \eqn{k} based on a weakly
+#'   informative Gaussian prior centered on 0.5. Defaults to `TRUE`.
+#' @param min_grid_pts The minimum number of grid points used in the fitting
+#'   algorithm. The actual number used is `min_grid_pts + floor(sqrt(length(x)))`.
+#' @param sort_x If `TRUE` (the default), the first step in the fitting
+#'   algorithm is to sort the elements of `x`. If `x` is already
+#'   sorted in ascending order then `sort_x` can be set to `FALSE` to
+#'   skip the initial sorting step.
+#' @return A named list with components `k` and `sigma`.
+#'
+#' @details Here the parameter \eqn{k} is the negative of \eqn{k} in Zhang &
+#'   Stephens (2009).
+#'
+#' @seealso [psis()], [pareto-k-diagnostic]
+#'
+#' @references
+#' Zhang, J., and Stephens, M. A. (2009). A new and efficient estimation method
+#' for the generalized Pareto distribution. *Technometrics* **51**, 316-325.
+#'
+gpdfit <- function(x, wip = TRUE, min_grid_pts = 30, sort_x = TRUE) {
+  posterior::gpdfit(
+    x = x,
+    wip = wip,
+    min_grid_pts = min_grid_pts,
+    sort_x = sort_x
+  )
+}

man/gpdfit.Rd

@@ -11,11 +11,3 @@ test_that("gpdfit returns correct result", {
   expect_snapshot_value(gpdfit_val_wip_default_grid, style = "serialize")
 })
 
-test_that("qgpd returns the correct result ", {
-  probs <- seq(from = 0, to = 1, by = 0.25)
-  q1 <- qgpd(probs, k = 1, sigma = 1)
-  expect_equal(q1, c(0, 1 / 3, 1, 3, Inf))
-
-  q2 <- qgpd(probs, k = 1, sigma = 0)
-  expect_true(all(is.nan(q2)))
-})