ppgmmga: Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms

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: 1.2
Depends: R (≥ 3.4)
Imports: Rcpp (≥ 1.0.0), mclust (≥ 5.4), GA (≥ 3.1), ggplot2 (≥ 2.2.1), ggthemes (≥ 3.4.0), cli, crayon, utils, stats
LinkingTo: Rcpp, RcppArmadillo (≥ 0.7)
Suggests: knitr (≥ 1.8)
Published: 2019-07-08
Author: Alessio Serafini ORCID iD [aut, cre], Luca Scrucca ORCID iD [aut]
Maintainer: Alessio Serafini <srf.alessio at gmail.com>
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

Downloads:

Reference manual: ppgmmga.pdf
Vignettes: A quick tour of ppgmmga
Package source: ppgmmga_1.2.tar.gz
Windows binaries: r-devel: ppgmmga_1.2.zip, r-release: ppgmmga_1.2.zip, r-oldrel: ppgmmga_1.2.zip
OS X binaries: r-release: ppgmmga_1.2.tgz, r-oldrel: ppgmmga_1.2.tgz
Old sources: ppgmmga archive

Linking:

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