Estimation for multivariate normal and Student-t data with monotone missingness CRAN Task View: Bayesian (http://CRAN.R-project.org/view=Bayesian) CRAN Task View: Multivariate (http://CRAN.R-project.org/view=Multivariate) 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 the lasso (double-exponential) and Normal-Gamma priors, and Student-t errors. 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), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided Software Depends: R (>= 2.10), pls, lars, MASS Suggests: quadprog, mvtnorm, accuracy Robert B. Gramacy <rbgramacy@chicagobooth.edu> Robert B. Gramacy <rbgramacy@chicagobooth.edu> LGPL 2012-04-19 application/tgz http://CRAN.R-project.org/package=monomvn