Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.

Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.

Estimate and understand individual-level variation in
    transmission. Implements density and cumulative compound Poisson
    discrete distribution functions (Kremer et al. (2021) 
    <doi:10.1038/s41598-021-93578-x>), as well as functions to calculate
    infectious disease outbreak statistics given epidemiological
    parameters on individual-level transmission; including the probability
    of an outbreak becoming an epidemic/extinct (Kucharski et al. (2020) 
    <doi:10.1016/S1473-3099(20)30144-4>), or the cluster size
    statistics, e.g. what proportion of cases cause X\% of transmission
    (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>).

The Patient Rule Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.