monomvn: Estimation for Multivariate Normal and Student-t Data with Monotone Missingness
Version 1.9-7

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 <>
Date of publication2017-01-08 15:02:13
MaintainerRobert B. Gramacy <>
Package repositoryView on CRAN
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monomvn documentation built on May 30, 2017, 7:58 a.m.