Estimation for multivariate normal data with monotone missingness
Description
Estimation of multivariate normal 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 an
(almost) arbitrary amount of missing data. The current version
supports maximum likelihood inference and an alpha implementation
of a Bayesian version employing a Bayesian lasso. A fully
functional standalone (alpha) interface to the Bayesian lasso
(from Park & Casella) is also provided