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.


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install.packages("monomvn")

1.9-9 by Robert B. Gramacy, 13 days ago


http://bobby.gramacy.com/r_packages/monomvn


Browse source code at https://github.com/cran/monomvn


Authors: Robert B. Gramacy <[email protected]>


Documentation:   PDF Manual  


Task views: Bayesian Inference, Multivariate Statistics


LGPL license


Imports quadprog, mvtnorm

Depends on pls, lars, MASS


Suggested by hetGP.


See at CRAN