Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.8-4
Date: 2010-04-23
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Description: 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), Normal-Gamma (from Griffin & Brown), and ridge
regression with model selection via Reversible Jump, and
student-t errors (from Geweke) is also provided
Depends: R (>= 2.4), pls, lars, MASS
Suggests: quadprog, mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: 2011-02-25 16:10:07 UTC; rgramacy
Repository: CRAN
Date/Publication: 2011-02-26 08:22:18

Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.8-5
Date: 2010-03-07
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Description: 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), Normal-Gamma (from Griffin & Brown), and ridge
regression with model selection via Reversible Jump, and
student-t errors (from Geweke) is also provided
Depends: R (>= 2.4), pls, lars, MASS
Suggests: quadprog, mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: 2011-03-07 19:31:59 UTC; rgramacy
Repository: CRAN
Date/Publication: 2011-03-08 07:27:03