Title: Collection of Packages for Internet Marketing
Description: Collection of packages for work with API 'Google Ads' <https://developers.google.com/google-ads/api/docs/start>,
'Yandex Direct' <https://yandex.ru/dev/direct/>, 'Yandex Metrica' <https://yandex.ru/dev/metrika/>,
'Facebook' <https://developers.facebook.com/docs/marketing-apis/> and 'AppsFlyer' <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>.
This packages allows you loading data from ads account and manage your ads materials.
(>= 1.4.2), gargle (>= 1.2.0), ryandexdirect, rfacebookstat,
rvkstat, rmytarget, rym, getProxy, rgoogleads, rappsflyer
Author: Alexey Seleznev [aut, cre] (<https://orcid.org/0000-0003-0410-7385>),

Title: Genetic algorithms and C-steps based LTS estimation
Description: This package includes the ga.lts function that estimates
LTS parameters using genetic algorithms and C-steps

Package: galts
Type: Package
Title: Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Version: 1.1
Date: 2011-02-04
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.
Depends: genalg, DEoptim
Repository: CRAN
License: GPL
LazyLoad: yes
Packaged: 2011-02-04 21:00:15 UTC; hako
Date/Publication: 2011-02-05 17:08:13

Package: galts
Type: Package
Title: Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Version: 1.2
Date: 2012-01-06
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.
Depends: genalg, DEoptim
Repository: CRAN
License: GPL
LazyLoad: yes
Packaged: 2012-01-06 21:17:54 UTC; hako
Date/Publication: 2012-01-07 06:51:11