Finds 'CRAN' packages by the topic requested. The topic can be given as a character string or as a regular expression and will help users to locate 'CRAN' packages matching their specified requirement. findPackage(<string>) returns a data frame of packages with description containing the input string.

Package designed to find an acceptable python binary.

Scans all directories and subdirectories of a path for code snippets, R scripts,
    R Markdown, PDF or text files containing a specific pattern.  Files found can be copied to a new folder.

Developed by CDC/ATSDR (Centers for Disease Control and Prevention/
    Agency for Toxic Substances and Disease Registry), 
    Social Vulnerability Index (SVI) serves as a tool to assess the resilience 
    of communities by taking into account socioeconomic and demographic factors. 
    Provided with year(s), region(s) and a geographic level of interest, 
    'findSVI' retrieves required variables from US census data and calculates SVI 
    for communities in the specified area based on CDC/ATSDR SVI documentation. 
    Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, 
    Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.

A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.

Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.

Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.