Package: monomvn
Type: Package
Title: Estimation for multivariate normal and Student-t data with
        monotone missingness
Version: 1.7-2
Date: 2008-06-02
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: Tue Jun 2 10:00:34 2009; bobby
Repository: CRAN
Date/Publication: 2009-06-02 17:29:17
