Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.9-4
Date: 2013-04-17
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 Gibbs sampling.  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), Horseshoe (from Carvalho, Polson, & Scott),
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
License: LGPL
URL: http://faculty.chicagobooth.edu/robert.gramacy/monomvn.html
Packaged: 2013-04-18 16:51:58 UTC; bobby
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-04-18 21:41:28

Package: monomvn
Type: Package
Title: Estimation for Multivariate Normal and Student-t Data with
Monotone Missingness
Version: 1.9-6
Date: 2016-02-10
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 Gibbs sampling.
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),
Horseshoe (from Carvalho, Polson, & Scott), and ridge regression
with model selection via Reversible Jump, and student-t errors
(from Geweke) is also provided.
Depends: R (>= 2.14.0), pls, lars, MASS
Imports: quadprog, mvtnorm
License: LGPL
URL: http://bobby.gramacy.com/r_packages/monomvn
NeedsCompilation: yes
Packaged: 2016-02-10 21:11:47 UTC; bobby
Repository: CRAN
Date/Publication: 2016-02-11 00:43:05