distributions3 @ useR! 2022

Abstract

(Authors: Achim Zeileis, Moritz N. Lang, Alex Hayes)

The distributions3 package provides a beginner-friendly and lightweight interface to probability distributions. It allows to create distribution objects in the S3 paradigm that are essentially data frames of parameters, for which standard methods are available: e.g., evaluation of the probability density, cumulative distribution, and quantile functions as well as random samples. It has been designed such that it can be employed in introductory statistics and probability courses. By not only providing objects for a single distribution but also for vectors of distributions, users can transition seamlessly to a representation of probabilistic forecasts from regression models such as GLM (generalized linear model), GAMLSS (generalized additive models for location, scale, and shape), etc. We show how the package can be used both in teaching and in applied statistical modeling, for interpreting fitted models and assessing their goodness of fit (“by hand” and via the topmodels package).

Resources

Links to: PDF slides, YouTube video, R code, vignette/blog post.