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: 1.9-7
Depends: R (≥ 2.14.0), pls, lars, MASS
Imports: quadprog, mvtnorm
Published: 2017-01-08
Author: Robert B. Gramacy
Maintainer: Robert B. Gramacy <rbg 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_1.9-7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: monomvn_1.9-7.tgz, r-oldrel: monomvn_1.9-7.tgz
Old sources: monomvn archive


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