monomvn: Estimation for MVN and Student-t Data with Monotone Missingness

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) <doi:10.1214/10-BA602>. 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: 1.9-13
Depends: R (≥ 2.14.0), pls, lars, MASS
Imports: quadprog, mvtnorm
Published: 2019-11-27
Author: Robert B. Gramacy, with Fortran contributions from Cleve Moler (dpotri/LINPACK) as updated by Berwin A. Turlach (qpgen2/quadprog)
Maintainer: Robert B. Gramacy <rbg at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: monomvn results


Reference manual: monomvn.pdf


Package source: monomvn_1.9-13.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): monomvn_1.9-13.tgz, r-release (x86_64): monomvn_1.9-13.tgz, r-oldrel: monomvn_1.9-13.tgz
Old sources: monomvn archive

Reverse dependencies:

Reverse suggests: hetGP


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