Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.1
Date: 2007-08-13
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Description: Estimation of multivariate normal data of arbitrary dimension
where the pattern of missing data is monotone. Through the use of
partial least squares and principal component regressions,
where standard regressions fail,
the package can handle an (almost) arbitrary amount of missing
data.  The current version supports maximum likelihood inference.
Future versions will provide a means of sampling from a Bayesian
posterior.
Depends: R (>= 2.5), pls, lars
Suggests: mvtnorm, accuracy
License: LGPL (Lesser GNU Public License)
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Mon Aug 13 09:43:07 2007; bobby

Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.2
Date: 2008-05-14
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
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
Depends: R (>= 2.5), pls, lars, MASS
Suggests: mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Mon May 19 11:31:00 2008; bobby

Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.3-1
Date: 2008-07-19
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
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 (beta) interface to the Bayesian lasso
(from Park & Casella) with model selection is also provided
Depends: R (>= 2.5), pls, lars, MASS
Suggests: mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Fri Jun 20 10:40:05 2008; hornik