Package: monomvn
Type: Package
Title: Estimation for Multivariate Normal and Student-t Data with
        Monotone Missingness
Version: 1.9-7
Date: 2016-12-28
Author: Robert B. Gramacy <rbg@vt.edu>
Maintainer: Robert B. Gramacy <rbg@vt.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 (>= 2.14.0), pls, lars, MASS
Imports: quadprog, mvtnorm
License: LGPL
URL: http://bobby.gramacy.com/r_packages/monomvn
NeedsCompilation: yes
Packaged: 2017-01-06 01:58:06 UTC; bobby
Repository: CRAN
Date/Publication: 2017-01-08 15:02:13
Built: R 3.3.3; x86_64-w64-mingw32; 2018-04-22 15:09:10 UTC; windows
Archs: i386, x64
