Implementation of the race/ethnicity prediction method, described 
    in "rethnicity: An R package for predicting ethnicity from names" 
    by Fangzhou Xie (2022) <doi:10.1016/j.softx.2021.100965> and 
    "Rethnicity: Predicting Ethnicity from Names" by Fangzhou Xie (2021) <arXiv:2109.09228>.

Interface to 'Python' modules, classes, and functions. When calling
    into 'Python', R data types are automatically converted to their equivalent 'Python'
    types. When values are returned from 'Python' to R they are converted back to R
    types. Compatible with all versions of 'Python' >= 2.7.

Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.

Provides tools for working with Type S (Sign) and
    Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
    <doi:10.1007/s001800000040> and
    Gelman & Carlin (2014) <doi:10.1177/1745691614551642>.
    In addition to simply calculating the probability of
    Type S/M error, the package includes functions for calculating these errors
    across a variety of effect sizes for comparison, and recommended sample size
    given "tolerances" for Type S/M errors. To improve the speed of these
    calculations, closed forms solutions for the probability of a Type S/M error
    from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132>
    are implemented. As of 1.0.0, this includes
    support only for simple research designs. See the
    package vignette for a fuller exposition on how Type S/M errors arise in
    research, and how to analyze them using the type of design analysis proposed
    in the above papers.

Assist in reproducible retrospective (ex-post) harmonization
    of data, particularly individual level survey data, by providing tools
    for organizing metadata, standardizing the coding of variables, and
    variable names and value labels, including missing values, and
    documenting the data transformations, with the help of comprehensive
    s3 classes.