Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.0-2
Date: 2007-07-10
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
Suggests: mvtnorm, accuracy
License: LGPL (Lesser GNU Public License)
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Tue Jul 10 11:06:21 2007; bobby

Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.0-3
Date: 2007-08-03
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
Suggests: mvtnorm, accuracy
License: LGPL (Lesser GNU Public License)
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: Sat Aug  4 10:03:48 2007; hornik

Package: monomvn
Type: Package
Title: Estimation for multivariate normal data with monotone missingness
Version: 1.0
Date: 2007-06-11
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
Suggests: mvtnorm, accuracy
License: LGPL (Lesser GNU Public License)
Packaged: Mon Jun 11 11:34:49 2007; bobby