Package: monomvn
Type: Package
Title: Estimation for MVN and Student-t Data with Monotone Missingness
Version: 1.9-17
Date: 2023-01-06
Author: Robert B. Gramacy <rbg@vt.edu>, with Fortran contributions from Cleve Moler (dpotri/LINPACK) as updated by Berwin A. Turlach (qpgen2/quadprog)
Maintainer: Robert B. Gramacy <rbg@vt.edu>
Description: Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <arXiv:0907.2135>.
 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: https://bobby.gramacy.com/r_packages/monomvn/
NeedsCompilation: yes
Packaged: 2022-12-28 19:38:18 UTC; bobby
Repository: CRAN
Date/Publication: 2023-01-06 15:40:02 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 13:49:13 UTC; windows
Archs: i386, x64
