Package: ppgmmga 1.3

Luca Scrucca

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>.

Authors:Alessio Serafini [aut], Luca Scrucca [aut, cre]

ppgmmga_1.3.tar.gz
ppgmmga_1.3.zip(r-4.7)ppgmmga_1.3.zip(r-4.6)ppgmmga_1.3.zip(r-4.5)
ppgmmga_1.3.tgz(r-4.6-x86_64)ppgmmga_1.3.tgz(r-4.6-arm64)ppgmmga_1.3.tgz(r-4.5-x86_64)ppgmmga_1.3.tgz(r-4.5-arm64)
ppgmmga_1.3.tar.gz(r-4.7-arm64)ppgmmga_1.3.tar.gz(r-4.7-x86_64)ppgmmga_1.3.tar.gz(r-4.6-arm64)ppgmmga_1.3.tar.gz(r-4.6-x86_64)
ppgmmga_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ppgmmga/json (API)

# Install 'ppgmmga' in R:

install.packages('ppgmmga', repos = c('https://luca-scr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/luca-scr/ppgmmga/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

4.00 score 2 stars 8 scripts 555 downloads 14 exports 25 dependencies

Last updated from:d31378af99. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	190
linux-devel-x86_64	NOTE	152
source / vignettes	OK	203
linux-release-arm64	NOTE	144
linux-release-x86_64	NOTE	182
macos-release-arm64	NOTE	129
macos-release-x86_64	NOTE	225
macos-oldrel-arm64	NOTE	105
macos-oldrel-x86_64	NOTE	230
windows-devel	NOTE	123
windows-release	NOTE	121
windows-oldrel	NOTE	124
wasm-release	OK	133

Exports:EntropyGauss EntropyGMM EntropyMC EntropySOTE EntropyUT EntropyVAR logsumexp nclass.numpy plot.ppgmmga ppgmmga ppgmmga.options print.ppgmmga print.summary.ppgmmga summary.ppgmmga

Dependencies:cli codetools cpp11 crayon farver foreach GA ggplot2 glue gtable isoband iterators labeling lifecycle mclust R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales vctrs viridisLite withr

A quick tour of ppgmmga

Rendered fromppgmmga.Rmdusingknitr::rmarkdown

Last update: 2024-09-19
Started: 2018-09-20

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation	ppgmmga-package
Compute the Number of Classes for a Histogram	nclass.numpy
Plots the data onto the projection subspace estimated by the ppgmmga algorithm	plot.ppgmmga
Projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation	ppgmmga print.ppgmmga
Class ''ppgmmga''	ppgmmga-class
Default values for 'ppgmmga' package	ppgmmga.options
Summary for projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation	print.summary.ppgmmga summary.ppgmmga