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/>, 
    'MyTarget' <https://target.my.com/help/advertisers/api_arrangement/ru>, 'Vkontakte' <https://vk.com/dev/methods>, 
    '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.

Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. Includes well-known matrices for benchmarking and testing the performance, stability, and accuracy of linear algebra algorithms. Inspired by 'MATLAB' 'gallery' functions.

The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.

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.