A comprehensive reproducibility framework designed for R and bioinformatics
    workflows. Automatically captures the entire analysis environment including R session
    info, package versions, external tool versions ('Samtools', 'STAR', 'BWA', etc.), 'conda'
    environments, reference genomes, data provenance with smart checksumming for large files,
    parameter choices, random seeds, and hardware specifications. Generates executable scripts
    with 'Docker', 'Singularity', and 'renv' configurations. Integrates with workflow managers
    ('Nextflow', 'Snakemake', 'WDL', 'CWL') to ensure complete reproducibility of computational
    research workflows.

Git hook scripts are useful for identifying simple issues before submission to code review. 'captain' (hook) is an R package to manage and run git pre-commit hooks.

Calibration of penalized criteria for model selection. The calibration methods available in this package are based on the slope heuristics.

Fast and user-friendly estimation of generalized linear models with
    multiple fixed effects and cluster the standard errors. The method to obtain
    the estimated fixed-effects coefficients is based on Stammann (2018) 
    <doi:10.48550/arXiv.1707.01815>, Gaure (2013)
    <doi:10.1016/j.csda.2013.03.024>, Berge (2018)
    <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020)
    <doi: 10.1177/1536867X20909691>.

Functions to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage, 2019.

Computer algebra via the 'SymPy' library (<https://www.sympy.org/>). 
  This makes it possible to solve equations symbolically, 
  find symbolic integrals, symbolic sums and other important quantities.

Tools for implementing covariate-adjusted response-adaptive procedures for binary, continuous and survival responses. Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references:
    Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007)<doi:10.1214/009053606000001424>
    Zhang, L. X. & Hu, F. (2009) <doi:10.1007/s11766-009-0001-6>
    Hu, J., Zhu, H., & Hu, F. (2015) <doi:10.1080/01621459.2014.903846>
    Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) <doi:10.1002/pst.2160>
    Mukherjee, A., Jana, S., & Coad, S. (2024) <doi:10.1177/09622802241287704>.