Package: ppgmmga 1.3

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-4.5)ppgmmga_1.3.zip(r-4.4)ppgmmga_1.3.zip(r-4.3)
ppgmmga_1.3.tgz(r-4.4-x86_64)ppgmmga_1.3.tgz(r-4.4-arm64)ppgmmga_1.3.tgz(r-4.3-x86_64)ppgmmga_1.3.tgz(r-4.3-arm64)
ppgmmga_1.3.tar.gz(r-4.5-noble)ppgmmga_1.3.tar.gz(r-4.4-noble)
ppgmmga_1.3.tgz(r-4.4-emscripten)ppgmmga_1.3.tgz(r-4.3-emscripten)
ppgmmga.pdf |ppgmmga.html✨
ppgmmga/json (API)
NEWS

# 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 152 downloads 14 exports 36 dependencies

Last updated 5 months agofrom:d31378af99. Checks:7 OK, 2 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Jan 16 2025
R-4.5-win-x86_64	NOTE	Jan 16 2025
R-4.5-linux-x86_64	NOTE	Jan 16 2025
R-4.4-win-x86_64	OK	Jan 16 2025
R-4.4-mac-x86_64	OK	Jan 16 2025
R-4.4-mac-aarch64	OK	Jan 16 2025
R-4.3-win-x86_64	OK	Jan 16 2025
R-4.3-mac-x86_64	OK	Jan 16 2025
R-4.3-mac-aarch64	OK	Jan 16 2025

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 colorspace crayon fansi farver foreach GA ggplot2 glue gtable isoband iterators labeling lattice lifecycle magrittr MASS Matrix mclust mgcv munsell nlme pillar pkgconfig R6 RColorBrewer Rcpp RcppArmadillo rlang scales tibble utf8 vctrs viridisLite withr

A quick tour of ppgmmga

Alessio Serafini, Luca Scrucca

Rendered fromppgmmga.Rmdusingknitr::rmarkdownon Jan 16 2025.

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