monomvn: Estimation for multivariate normal and Student-t data with monotone missingness

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

Version: 1.9-4
Depends: R (≥ 2.10), pls, lars, MASS
Suggests: quadprog, mvtnorm
Published: 2013-04-18
Author: Robert B. Gramacy
Maintainer: Robert B. Gramacy <rbgramacy at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: ChangeLog
In views: Bayesian, Multivariate
CRAN checks: monomvn results


Reference manual: monomvn.pdf
Package source: monomvn_1.9-4.tar.gz
OS X binary: monomvn_1.9-4.tgz
Windows binary:
Old sources: monomvn archive