Package: galts
Type: Package
Title: Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)
Estimation
Version: 1.3.1
Date: 2017-11-24
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>
Description: 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.
Depends: genalg, DEoptim
Repository: CRAN
License: GPL
LazyLoad: yes
Packaged: 2017-11-24 10:35:50 UTC; hako
Date/Publication: 2017-11-24 10:49:11 UTC
NeedsCompilation: no

Package: galts
Type: Package
Title: Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Version: 1.3
Date: 2013-02-06
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>
Description: This package 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
included the function medmad for fast outlier detection in
linear regression.
Depends: genalg, DEoptim
Repository: CRAN
License: GPL
LazyLoad: yes
Packaged: 2013-02-06 20:25:43 UTC; hako
Date/Publication: 2013-02-07 09:27:39