Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.8-8
Date: 2011-12-18
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 (>= 2.14.0), pls, lars, MASS
Suggests: quadprog, mvtnorm, accuracy
License: LGPL
URL: http://faculty.chicagobooth.edu/robert.gramacy/monomvn.html
Packaged: 2011-12-18 16:08:00 UTC; rgramacy
Repository: CRAN
Date/Publication: 2011-12-19 11:17:19

Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.8-9
Date: 2012-01-07
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 (>= 2.10), pls, lars, MASS
Suggests: quadprog, mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: 2012-01-07 17:14:13 UTC; bobby
Repository: CRAN
Date/Publication: 2012-01-07 17:54:55