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 istanbul.edu.tr>
License:	GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation:	no
CRAN checks:	galts results

Documentation:

Reference manual:

galts.pdf

Downloads:

Package source:	galts_1.3.2.tar.gz
Windows binaries:	r-devel: galts_1.3.2.zip, r-release: galts_1.3.2.zip, r-oldrel: galts_1.3.2.zip
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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