% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sim.data.FCGGUM.R
\name{model.FCGGUM}
\alias{model.FCGGUM}
\title{Compute FCGGUM Forced-Choice Pattern Probabilities}
\usage{
model.FCGGUM(theta, par, patterns.total, patterns)
}
\arguments{
\item{theta}{Latent trait matrix.}

\item{par}{Binary GGUM item parameter matrix with columns
\code{a1..aD, delta1..deltaD, tau0, tau1}; \code{tau0} is fixed at 0
and \code{tau1 < 0}.}

\item{patterns.total}{Full ranking patterns.}

\item{patterns}{Observed ranking patterns.}
}
\value{
Block ranking probability matrix. The code first computes binary
GGUM endorsement probabilities from the distance-based MGGUM formula,
transforms them to logits, and then applies the sequential
Luce/Plackett rule. MOLE and PICK probabilities are sums over
compatible full rankings normalized over observed patterns.
}
\description{
Compute FCGGUM Forced-Choice Pattern Probabilities
}
\keyword{internal}
