ppgmmga: Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms (original) (raw)
Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.
| Version: | 1.3 |
|---|---|
| Depends: | R (≥ 3.4) |
| Imports: | Rcpp (≥ 1.0.0), mclust (≥ 5.4), GA (≥ 3.1), ggplot2 (≥ 2.2.1), cli, crayon, utils, stats |
| LinkingTo: | Rcpp, RcppArmadillo (≥ 0.7) |
| Suggests: | knitr (≥ 1.8), rmarkdown (≥ 2.0) |
| Published: | 2023-11-17 |
| DOI: | 10.32614/CRAN.package.ppgmmga |
| Author: | Alessio Serafini  [aut], Luca Scrucca [aut, cre] |
| Maintainer: | Luca Scrucca <luca.scrucca at unipg.it> |
| BugReports: | https://github.com/luca-scr/ppgmmga/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/luca-scr/ppgmmga |
| NeedsCompilation: | yes |
| Citation: | ppgmmga citation info |
| Materials: | README NEWS |
| CRAN checks: | ppgmmga results |
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