Calculates the Most Probable Number (MPN) to quantify the
    concentration (density) of microbes in serial dilutions of a laboratory
    sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>).
    Also calculates the Aerobic Plate Count (APC) for similar microbial
    enumeration experiments.

Symbolic computing with multivariate polynomials in R.

A flexible framework for power analysis using Monte
    Carlo simulation for settings in which considerations of the correlations
    between predictors are important. Users can set up a data generative model
    that preserves dependence structures among predictors given existing data
    (continuous, binary, or ordinal). Users can also generate power curves to
    assess the trade-offs between sample size, effect size, and power of a design.
    This package includes several statistical models common in environmental
    mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.

Analysis of experimental multi-parent populations to detect regions
             of the genome (called quantitative trait loci, QTLs) influencing
             phenotypic traits measured in unique and multiple environments.
             The population must be composed of crosses between a set of at
             least three parents (e.g. factorial design, 'diallel', or nested
             association mapping). The functions cover data processing,
             QTL detection, and results visualization. The implemented methodology
             is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017)
             <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti
             and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>,
             and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.

Fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards. See Burke & MacKenzie (2016) <doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.