% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/logL_gau_missing.R
\name{.logL_gau_missing}
\alias{.logL_gau_missing}
\title{Compute observed-data log-likelihood for AD with missing values}
\usage{
.logL_gau_missing(y, order, mu, phi, sigma, blocks = NULL, tau = 0)
}
\arguments{
\item{y}{Data matrix (n_subjects x n_time), may contain NA}

\item{order}{Antedependence order (0, 1, or 2)}

\item{mu}{Mean vector (length n_time)}

\item{phi}{Dependence parameter(s)}

\item{sigma}{Innovation standard deviations (length n_time)}

\item{blocks}{Block membership vector (optional)}

\item{tau}{Block effects, first element constrained to zero}
}
\value{
Observed-data log-likelihood (scalar)
}
\description{
Uses multivariate normal marginalization to compute the likelihood
of observed values, marginalizing over missing values.
}
\keyword{internal}
