Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.9-2
Date: 2012-07-01
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: 2012-07-01 07:58:46 UTC; bobby
Repository: CRAN
Date/Publication: 2012-07-01 09:18:07

Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.9-3
Date: 2012-10-25
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: 2012-10-25 14:46:32 UTC; bobby
Repository: CRAN
Date/Publication: 2012-10-25 15:45:33