swMATH ID: 8173
Software Authors: Robert B. Gramacy
Description: 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
Homepage: http://cran.r-project.org/web/packages/monomvn/index.html
Source Code:  https://github.com/cran/Monomvn
Dependencies: R
Related Software: R; glmnet; glasso; ElemStatLearn; MASS (R); CRAN; BayesDA; lars; spikeSlabGAM; BayesReg; BayesianGLasso; BigQuic; glassoFast; cglasso; igraph; sparsebn; BGGM; bnstruct; bnlearn; BDgraph
Cited in: 15 Publications

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