monomvn: Estimation for MVN and Student-t Data with Monotone Missingness (original) (raw)
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: | 1.9-21 | |
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Depends: | R (≥ 2.14.0), pls, lars, MASS | |
Imports: | quadprog, mvtnorm | |
Published: | 2024-09-23 | |
DOI: | 10.32614/CRAN.package.monomvn | |
Author: | Robert B. Gramacy [aut, cre] (with Fortran contributions from Cleve Moler (dpotri/LINPACK) as updated by Berwin A. Turlach (qpgen2/quadprog)) | |
Maintainer: | Robert B. Gramacy | |
License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
URL: | https://bobby.gramacy.com/r_packages/monomvn/ | |
NeedsCompilation: | yes | |
Materials: | ||
In views: | MissingData | |
CRAN checks: | monomvn results |
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