% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/robust_lpa.R
\name{robust_lpa}
\alias{robust_lpa}
\title{Fit a Single Robust Latent Profile Analysis Model}
\usage{
robust_lpa(data, G, model = 6, max_iter = 100, tol = 1e-06, n_starts = 5)
}
\arguments{
\item{data}{A matrix or data.frame of observations.}

\item{G}{The number of latent profiles to extract.}

\item{model}{An integer (1 to 6) specifying the model parameterization.}

\item{max_iter}{Maximum number of EM iterations.}

\item{tol}{Tolerance for convergence.}

\item{n_starts}{Number of random initializations.}
}
\value{
A list containing parameters, fit indices, and assignments.
}
\description{
This function estimates a single robust Latent Profile Analysis (LPA) model
for a specified number of profiles and model structure. It automatically
handles missing data via robust FIML if present.
}
\examples{
# Quick example using a small subset of the iris dataset
data(iris)
fit <- robust_lpa(data = iris[1:30, 1:2], G = 2, model = 1, n_starts = 1)
# Print the fit indices
fit$fit
}
