DistributionOptimization: Distribution Optimization

Fits Gaussian Mixtures by applying evolution. As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The algorithm and applications of this package are published under: Lerch, F., Ultsch, A., Lotsch, J. (2020) <doi:10.1038/s41598-020-57432-w>. The evolution is based on the 'GA' package: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04> while the Gaussian Mixture Logic stems from 'AdaptGauss': Ultsch, A, et al. (2015) <doi:10.3390/ijms161025897>.

Version:	1.2.6
Imports:	ggplot2, GA, AdaptGauss, graphics, stats, utils, pracma
Suggests:	parallelDist
Published:	2020-02-12
Author:	Florian Lerch, Jorn Lotsch, Alfred Ultsch
Maintainer:	Florian Lerch <lerch at mathematik.uni-marburg.de>
License:	MIT + file LICENSE
NeedsCompilation:	no
CRAN checks:	DistributionOptimization results

Documentation:

Reference manual:

DistributionOptimization.pdf

Downloads:

Package source:	DistributionOptimization_1.2.6.tar.gz
Windows binaries:	r-devel: DistributionOptimization_1.2.6.zip, r-release: DistributionOptimization_1.2.6.zip, r-oldrel: DistributionOptimization_1.2.6.zip
macOS binaries:	r-release (arm64): DistributionOptimization_1.2.6.tgz, r-oldrel (arm64): DistributionOptimization_1.2.6.tgz, r-release (x86_64): DistributionOptimization_1.2.6.tgz, r-oldrel (x86_64): DistributionOptimization_1.2.6.tgz
Old sources:	DistributionOptimization archive

Reverse dependencies:

Reverse imports:

opGMMassessment

Linking:

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