Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.7-4
Date: 2009-11-16
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) prior 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) 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: 2009-11-16 21:16:20 UTC; bobby
Repository: CRAN
Date/Publication: 2009-11-17 20:29:35

Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
monotone missingness
Version: 1.7
Date: 2008-04-07
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) prior 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) 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: Wed Apr 15 08:47:05 2009; bobby
Repository: CRAN
Date/Publication: 2009-04-15 18:13:36