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.
|Depends:||R (≥ 2.14.0), pls, lars, MASS|
|Author:||Robert B. Gramacy|
|Maintainer:||Robert B. Gramacy <rbgramacy at chicagobooth.edu>|
|License:||LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]|
|In views:||Bayesian, Multivariate|
|CRAN checks:||monomvn results|
|Windows binaries:||r-devel: monomvn_1.9-6.zip, r-release: monomvn_1.9-6.zip, r-oldrel: monomvn_1.9-6.zip|
|OS X Mavericks binaries:||r-release: monomvn_1.9-6.tgz, r-oldrel: monomvn_1.9-6.tgz|
|Old sources:||monomvn archive|
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