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ppgmmga

An R package accompanying the paper Projection pursuit based on Gaussian mixtures and evolutionary algorithms by Luca Scrucca and Alessio Serafini (2018).

Installation

You can install the released version of ppgmmga from CRAN:

install.packages("ppgmmga")

or the development version from GitHub:

# install.packages("devtools")
devtools::install_github("luca-scr/ppgmmga", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"))

Usage

The methodology implemented in the package is describe in the paper referenced below.

Usage of the main functions and some examples are included in the vignette A quick tour of ppgmmga, which is available as

vignette("ppgmmga")

References

Scrucca, L. and Serafini, A. (2019) Projection pursuit based on Gaussian mixtures and evolutionary algorithms. Journal of Computational and Graphical Statistics, 28:4, 847–860. DOI: 10.1080/10618600.2019.1598871 URL https://doi.org/10.1080/10618600.2019.1598871

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Install

install.packages('ppgmmga')

Monthly Downloads

166

Version

1.3

License

GPL (>= 2)

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Maintainer

Last Published

November 17th, 2023

Functions in ppgmmga (1.3)

ppgmmga-internal

Internal ppgmmga functions
plot.ppgmmga

Plots the data onto the projection subspace estimated by the ppgmmga algorithm
ppgmmga-class

Class 'ppgmmga'
summary.ppgmmga

Summary for projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation
nclass.numpy

Compute the Number of Classes for a Histogram
ppgmmga-package

Projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation
ppgmmga

Projection pursuit based on Gaussian mixtures and evolutionary algorithms for data visualisation
ppgmmga.options

Default values for ppgmmga package