Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.8-1
Date: 2010-03-09
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: 2010-03-09 09:28:49 UTC; bobby
Repository: CRAN
Date/Publication: 2010-03-09 11:37:47

Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.8-10
Date: 2012-04-14
Author: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
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 (>= 2.10), pls, lars, MASS
Suggests: quadprog, mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: 2012-04-18 16:23:44 UTC; rgramacy
Repository: CRAN
Date/Publication: 2012-04-19 07:58:55