Package: galts 1.3.2

galts: Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares) Estimation

Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression.

Authors:Mehmet Hakan Satman

galts_1.3.2.tar.gz
galts_1.3.2.zip(r-4.7)galts_1.3.2.zip(r-4.6)galts_1.3.2.zip(r-4.5)
galts_1.3.2.tgz(r-4.6-any)galts_1.3.2.tgz(r-4.5-any)
galts_1.3.2.tar.gz(r-4.7-any)galts_1.3.2.tar.gz(r-4.6-any)
galts_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
galts/json (API)

# Install 'galts' in R:
install.packages('galts', repos = c('https://jbytecode.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jbytecode/galts/issues

On CRAN:

Conda:

2.70 score 9 scripts 145 downloads 3 exports 2 dependencies

Last updated from:5b5f466773. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR144
source / vignettesOK113
linux-release-x86_64ERROR100
macos-release-arm64ERROR128
macos-oldrel-arm64ERROR110
windows-develERROR96
windows-releaseERROR168
windows-oldrelERROR86
wasm-releaseOK114

Exports:ga.ltsmedmadmedmad.cov

Dependencies:DEoptimgenalg