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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:10.48550/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.
Version: | 1.9-20 |
Depends: | R (≥ 2.14.0), pls, lars, MASS |
Imports: | quadprog, mvtnorm |
Published: | 2024-01-11 |
DOI: | 10.32614/CRAN.package.monomvn |
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 vt.edu> |
License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
URL: | https://bobby.gramacy.com/r_packages/monomvn/ |
NeedsCompilation: | yes |
Materials: | ChangeLog |
In views: | MissingData |
CRAN checks: | monomvn results |
Reference manual: | monomvn.pdf |
Package source: | monomvn_1.9-20.tar.gz |
Windows binaries: | r-devel: monomvn_1.9-20.zip, r-release: monomvn_1.9-20.zip, r-oldrel: monomvn_1.9-20.zip |
macOS binaries: | r-release (arm64): monomvn_1.9-20.tgz, r-oldrel (arm64): monomvn_1.9-20.tgz, r-release (x86_64): monomvn_1.9-20.tgz, r-oldrel (x86_64): monomvn_1.9-20.tgz |
Old sources: | monomvn archive |
Reverse suggests: | flowml, hetGP, tidyfit |
Please use the canonical form https://CRAN.R-project.org/package=monomvn to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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