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An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
| Version: | 1.7 |
| Imports: | car, ggplot2, MASS, moments, parallel, penalized, plyr, reshape2, sn |
| Published: | 2022-10-05 |
| DOI: | 10.32614/CRAN.package.mvdalab |
| Author: | Nelson Lee Afanador, Thanh Tran, Lionel Blanchet, and Richard Baumgartner |
| Maintainer: | Nelson Lee Afanador <nelson.afanador at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | mvdalab results |
| Reference manual: | mvdalab.pdf |
| Package source: | mvdalab_1.7.tar.gz |
| Windows binaries: | r-devel: mvdalab_1.7.zip, r-release: mvdalab_1.7.zip, r-oldrel: mvdalab_1.7.zip |
| macOS binaries: | r-release (arm64): mvdalab_1.7.tgz, r-oldrel (arm64): mvdalab_1.7.tgz, r-release (x86_64): mvdalab_1.7.tgz, r-oldrel (x86_64): mvdalab_1.7.tgz |
| Old sources: | mvdalab archive |
| Reverse imports: | DTSR |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.