Provides vectorised functions for computing p-values of various
  common discrete statistical tests, as described e.g. in Agresti (2002)
  <doi:10.1002/0471249688>, including their distributions. Exact and
  approximate computation methods are provided. For exact p-values, several
  procedures of determining two-sided p-values are included, which are
  outlined in more detail in Hirji (2006) <doi:10.1201/9781420036190>.

Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.

A collection of supervised discretization
        algorithms. It can also be grouped in terms of top-down or
        bottom-up, implementing the discretization algorithms.

Bindings for additional classification models for use with
    the 'parsnip' package. Models include flavors of discriminant
    analysis, such as linear (Fisher (1936)
    <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized (Friedman
    (1989) <doi:10.1080/01621459.1989.10478752>), and flexible (Hastie,
    Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), as
    well as naive Bayes classifiers (Hand and Yu (2007)
    <doi:10.1111/j.1751-5823.2001.tb00465.x>).

Templates and data files to support "Discrete Choice Analysis with R",
    Páez, A. and Boisjoly, G. (2023) <doi:10.1007/978-3-031-20719-8>.

Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.

A simple approach to measure political sophistication based on open-ended survey responses. Discursive sophistication captures the complexity of individual attitude expression by quantifying its relative size, range, and constraint. For more information on the measurement approach see: Kraft, Patrick W. 2023. "Women Also Know Stuff: Challenging the Gender Gap in Political Sophistication." American Political Science Review (forthcoming).

Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.