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

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.

Package details

AuthorRobert B. Gramacy <[email protected]>
MaintainerRobert B. Gramacy <[email protected]>
Package repositoryView on CRAN
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monomvn documentation built on Sept. 21, 2018, 6:38 p.m.