Package: monomvn
Type: Package
Title: Estimation for Multivariate Normal and Student-t Data with
        Monotone Missingness
Version: 1.9-10
Date: 2019-06-10
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: 2019-06-10 14:59:53 UTC; bobby
Repository: CRAN
Date/Publication: 2019-06-11 16:20:03 UTC
Built: R 3.6.1; x86_64-w64-mingw32; 2019-12-12 20:25:37 UTC; windows
Archs: x64
