Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
        monotone missingness
Version: 1.8-6
Date: 2011-07-07
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 (>= 2.4), pls, lars, MASS
Suggests: quadprog, mvtnorm, accuracy
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html
Packaged: 2011-07-08 14:47:57 UTC; rgramacy
Repository: CRAN
Date/Publication: 2011-07-23 13:57:31
Built: R 2.12.2; i386-pc-mingw32; 2011-10-30 02:54:07 UTC; windows
Archs: i386, x64
