Estimation of multivariate normal 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 an (almost) arbitrary amount of missing data. The current version supports maximum likelihood inference and implementation of a Bayesian version employing a Bayesian lasso. A fully functional standalone interface to the Bayesian lasso (from Park & Casella) and ridge regression with model selection via Reversible Jump is also provided
| Version: | 1.5 |
| Depends: | R (≥ 2.4), pls, lars, MASS |
| Suggests: | quadprog, mvtnorm, accuracy |
| Date: | 2008-11-13 |
| Author: | Robert B. Gramacy |
| Maintainer: | Robert B. Gramacy <bobby at statslab.cam.ac.uk> |
| License: | LGPL |
| URL: | http://www.statslab.cam.ac.uk/~bobby/monomvn.html |
| In views: | Multivariate |
| CRAN checks: | monomvn results |
Downloads:
| Package source: | monomvn_1.5.tar.gz |
| MacOS X binary: | monomvn_1.5.tgz |
| Windows binary: | monomvn_1.5.zip |
| Reference manual: | monomvn.pdf |
| News/ChangeLog: | ChangeLog |
| Old sources: | monomvn archive |