A native R implementation of the F-UJI (FAIRsFAIR Research Data
    Object Assessment) and FRSM (FAIR for Research Software) metrics for
    evaluating how well a research data object or piece of research software
    satisfies the FAIR principles (Findable, Accessible, Interoperable,
    Reusable). The software metrics operationalize the FAIR Principles for
    Research Software (FAIR4RS) of Chue Hong et al. (2022)
    <doi:10.15497/RDA00068>. Given a persistent identifier, URL, or code
    repository, 'rfair'
    resolves it, harvests metadata from landing pages and registries, and scores
    it against the FAIRsFAIR metrics of Devaraju and Huber (2020)
    <doi:10.5281/zenodo.3775793> entirely in R, without requiring an external
    assessment server. 'rfair' began as a fork of the 'rfuji' F-UJI API client
    and reimplements the assessment engine natively.

Simulates the dynamics of exploited fish populations using the Jones modification of the Beverton-Holt 
             equilibrium yield equation to compute yield-per-recruit and dynamic pool models (Ricker 1975) 
             <https://publications.gc.ca/site/eng/480738/publication.html>. 
             Allows users to evaluate minimum, slot, and inverted length limits on exploited fisheries using 
             specified life history parameters. Users can simulate population under a variety of conditional fishing mortality 
             and conditional natural mortality. Calculated quantities include number of fish harvested and dying naturally, 
             mean weight and length of fish harvested, number of fish that reach specified lengths of interest, total number 
             of fish and biomass in the population, and stock density indices.

Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.