Package: monomvn 1.9-20

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

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) <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.

Authors:Robert B. Gramacy <[email protected]>, with Fortran contributions from Cleve Moler as updated by Berwin A. Turlach

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Last updated 8 months agofrom:2d2f8817d9. Checks:OK: 9. Indexed: yes.

Exports:add.pe.QPbhsblassobmonomvnbridgedefault.QPEllik.normkl.normmonomvnmonomvn.solve.QPrandmvnregressrmonormse.muSrwish

Dependencies:larsMASSmvtnormplsquadprog