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.

Version: 1.3.2
Depends: genalg, DEoptim
Published: 2023-08-20
DOI: 10.32614/CRAN.package.galts
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: galts results


Reference manual: galts.pdf


Package source: galts_1.3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): galts_1.3.2.tgz, r-oldrel (arm64): galts_1.3.2.tgz, r-release (x86_64): galts_1.3.2.tgz, r-oldrel (x86_64): galts_1.3.2.tgz
Old sources: galts archive


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