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 vt.edu> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| URL: | http://bobby.gramacy.com/r_packages/monomvn |
| 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: monomvn_1.9-7.zip, r-release: monomvn_1.9-7.zip, r-oldrel: monomvn_1.9-7.zip |
| OS X El Capitan binaries: | r-release: monomvn_1.9-7.tgz |
| OS X Mavericks binaries: | r-oldrel: monomvn_1.9-7.tgz |
| Old sources: | monomvn archive |
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