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

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galts.pdf |galts.html✨
galts/json (API)

Peer review:

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

On CRAN:

3 exports 0.63 score 2 dependencies 9 scripts 225 downloads

Last updated 1 years agofrom:5b5f466773. Checks:OK: 1 ERROR: 6. Indexed: yes.

Exports:ga.ltsmedmadmedmad.cov

Dependencies:DEoptimgenalg