% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrappers.R
\name{dpmix}
\alias{dpmix}
\title{Fit a one-arm Dirichlet process mixture without a GPD tail}
\usage{
dpmix(
  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{dpmix.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{dpmix()} is the one-step convenience wrapper for the bulk-only model.
It combines \code{\link{bundle}} and \code{\link{mcmc}} for one-arm data.
}
\details{
The fitted model targets the posterior predictive bulk distribution
\deqn{f(y \mid x) = \int f(y \mid x, \theta)\,d\Pi(\theta),}
without the spliced tail augmentation used by \code{\link{dpmgpd}}.

Use this wrapper when the outcome support is adequately modeled by the bulk
kernel alone. If you need threshold exceedance modeling or extreme-quantile
extrapolation, use \code{\link{dpmgpd}} instead.
}
\seealso{
\code{\link{build_nimble_bundle}},
\code{\link{bundle}}, \code{\link{dpmgpd}},
\code{\link{predict.mixgpd_fit}}, \code{\link{summary.mixgpd_fit}}.
}
