% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrappers.R
\name{dpmgpd}
\alias{dpmgpd}
\title{Fit a one-arm Dirichlet process mixture with a spliced GPD tail}
\usage{
dpmgpd(
  y = NULL,
  X = NULL,
  treat = NULL,
  data = NULL,
  mcmc = list(),
  formula = NULL,
  ...
)
}
\arguments{
\item{y}{Either a response vector or a bundle object.}

\item{X}{Optional design matrix/data.frame.}

\item{treat}{Optional binary treatment indicator. If supplied, this wrapper
errors; use \code{dpmgpd.causal()} for causal models.}

\item{data}{Optional data.frame used with \code{formula}.}

\item{mcmc}{Named list of run arguments passed to \code{mcmc()} (including
optional performance controls such as \code{parallel_chains},
\code{workers}, \code{timing}, and \code{z_update_every}).}

\item{formula}{Optional formula.}

\item{...}{Additional build arguments passed to \code{\link{build_nimble_bundle}}.}
}
\value{
A fitted object of class \code{"mixgpd_fit"}.
}
\description{
\code{dpmgpd()} is the one-step convenience wrapper for the spliced
bulk-tail model. It combines \code{\link{bundle}} and \code{\link{mcmc}} for
one-arm data.
}
\details{
This wrapper targets the posterior predictive distribution obtained by
combining a flexible bulk DPM with a generalized Pareto exceedance model
above the threshold \eqn{u(x)}. In the tail region the predictive density is
proportional to
\deqn{\{1 - p_u(x)\} f_{\mathrm{GPD}}(y \mid x), \qquad y > u(x),}
where \eqn{p_u(x)} is the posterior bulk mass below the threshold.

Use this wrapper when upper-tail behavior matters for inference, prediction,
or extrapolation of extreme quantiles and survival probabilities.
}
\seealso{
\code{\link{build_nimble_bundle}},
\code{\link{bundle}}, \code{\link{dpmix}},
\code{\link{predict.mixgpd_fit}}, \code{\link{summary.mixgpd_fit}}.
}
