\HeaderA{monomvn-package}{Estimation for Multivariate Normal and Student-t Data with Monotone Missingness}{monomvn.Rdash.package}
\keyword{package}{monomvn-package}
\begin{Description}\relax
Estimation of multivariate normal and student-t data of
arbitrary dimension where the pattern of missing data is monotone.
Through the use of parsimonious/shrinkage regressions
(plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
the package can handle a nearly arbitrary amount of missing data.
The current version supports maximum likelihood inference and
a full Bayesian approach employing scale-mixtures for the
lasso (double-exponential) and Normal-Gamma priors,
and Student-t errors.  Monotone 
data augmentation extends this Bayesian approach to arbitrary
missingness patterns.  A fully functional standalone interface to the 
Bayesian lasso (from Park \& Casella), the Normal-Gamma (from Griffin
\& Brown), and ridge regression with model 
selection via Reversible Jump, and student-t errors (from Geweke) is 
also provided
\end{Description}
\begin{Details}\relax
For a fuller overview including a complete list of functions, demos and
vignettes, please use \code{help(package="monomvn")}.
\end{Details}
\begin{Author}\relax
Robert B. Gramacy \email{bobby@statslab.cam.ac.uk}

Maintainer: 
Robert B. Gramacy \email{bobby@statslab.cam.ac.uk}
\end{Author}
\begin{References}\relax
Robert B. Gramacy, Joo Hee Lee and Ricardo Silva (2008).
\emph{On estimating covariances between many assets with histories 
of highly variable length}. \\ Preprint available on arXiv:0710.5837:
\url{http://arxiv.org/abs/0710.5837}

\url{http://www.statslab.cam.ac.uk/~bobby/monomvn.html}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{monomvn}{monomvn}}, the now defunct \code{norm} package, \pkg{mvnmle}
\end{SeeAlso}

