Estimation of multivariate normal and studentt 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 scalemixtures 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), NormalGamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and studentt errors (from Geweke) is also provided.
Package details 


Author  Robert B. Gramacy <rbg@vt.edu> 
Date of publication  20170108 15:02:13 
Maintainer  Robert B. Gramacy <rbg@vt.edu> 
License  LGPL 
Version  1.97 
URL  http://bobby.gramacy.com/r_packages/monomvn 
Package repository  View on CRAN 
Installation 
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