Database data model management utilities for R packages in the Observational Health Data Sciences and
    Informatics program <https://ohdsi.org>. 'ResultModelManager' provides utility functions to allow package
    maintainers to migrate existing SQL database models, export and import results in consistent patterns.

Using a CSV, LaTeX and R to easily build attractive resumes.

Implements the regularized exponentially tilted empirical 
    likelihood method. Details of the methods are given in Kim, MacEachern, and 
    Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by 
    the U.S. National Science Foundation under Grants No. SES-1921523 and 
    DMS-2015552.

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) <doi:10.48550/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.