Estimation for MVN and Student-t Data with Monotone Missingness R package monomvn version 1.9-21 CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) 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) <doi:10.48550/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. Software Depends: R (>= 2.14.0), pls, lars, MASS Imports: quadprog, mvtnorm Robert B. Gramacy <rbg@vt.edu> Comprehensive R Archive Network (CRAN) Robert B. Gramacy [aut, cre] (with Fortran contributions from Cleve Moler (dpotri/LINPACK) as updated by Berwin A. Turlach (qpgen2/quadprog)) LGPL 2024-09-23 application/tgz https://CRAN.R-project.org/package=monomvn doi:10.32614/CRAN.package.monomvn