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DistributionOptimization

1.2.6Package record

Distribution Optimization

1packages depend
2.8Kdownloads / year
–test coverage
11/13flavors OK

Overview

About

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.

Install

Health

CRAN checks
2NOTE11OK
Check messages
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Florian Lerch <lerch@mathematik.uni-marburg.de>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Florian",
                      family = "Lerch",
                      role = c("aut", "cre"),
                      email = "lerch@mathematik.uni-marburg.de"),
               person(given = "Jorn",
                      family = "Lotsch",
                      role = "aut"),
               person(given = "Alfred",
                      family = "Ultsch",
                      role = "aut"))
as necessary.

CRAN check page ↗

Slowest check: 1.9 min · r-oldrel-windows-x86_64
Status changes
  • NOTE11 OK, 2 NOTE2026-06-09
  • ERROR11 OK, 1 NOTE, 1 ERROR2026-06-08
  • NOTE9 OK, 5 NOTE2026-03-10
Advisories

none known

Documentation

In release 1.2.6
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Description cites a JSS article
Help pages
5includes a package overview page
Examples that run
75%3 run in a plain check · 1 never run
Exported functions with examples
100%
Documented parameters
89%
Return-value docs
100%
Help pages citing references
20%

Downloads

2.8K
CRAN downloads in the past year
Rank #15,832 · ~8/day · ~237/mo
Daily download trend is not available in this view yet.
31730 days
77290 days
2.8K1 year
Compare downloads with other packages →
Also on82 r2u4 autocran

Dependencies

Declared dependencies
5 external dependencies (excludes base and recommended)

Installing DistributionOptimization adds 81 packages (46 with compiled code, 84 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (0)
none
Imports (7)
ggplot2GAAdaptGaussgraphicsstatsutilspracma
LinkingTo (0)
none
Suggests (1)
Enhances (0)
none
Reverse dependencies
1direct
1indirect
Imports 1
Depends, Imports or LinkingTo, most downloaded first

People & History

People (from the free-text Author field) (3)

Maintainer since 1.2.1 (2019)

Releases (3)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • 1.2.6Latest
    record
    2020-02-12 · current release · diff ↗
  • 1.2.4
    record
    2019-11-25 · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • 1.2.1
    record
    2019-03-18
  • R
    R 3.5.0 released · 2018-04-23

Package metadata

First published
2019-03-18
Total releases
3 / 7 yrs
License
MIT + file LICENSE OSI
Download size
11 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("DistributionOptimization")

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for DistributionOptimization version 1.2.6 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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