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) .

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