Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.3
Date: 2008-07-09
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: Tue Jun 10 00:02:41 2008; bobby

Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.4-1
Date: 2008-09-18
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 beta implementation
of a Bayesian version employing a Bayesian lasso.  A fully
functional standalone (beta) interface to the Bayesian lasso
(from Park & Casella) and ridge regression with model selection
via Reversible Jump is also provided
Depends: R (>= 2.4), pls, lars, MASS
Suggests: mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Thu Sep 18 10:18:22 2008; bobby

Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.4
Date: 2008-07-20
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 beta implementation
of a Bayesian version employing a Bayesian lasso.  A fully
functional standalone (beta) interface to the Bayesian lasso
(from Park & Casella) and ridge regression with model selection
via Reversible Jump is also provided
Depends: R (>= 2.4), pls, lars, MASS
Suggests: mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Sun Jul 20 08:19:50 2008; bobby