distributions3: From Basic Probability to Probabilistic Regression (useR! 2022)
Channel: Achim Zeileis
Date: 23/06/2022

Prerecorded version of my useR! 2022 presentation on the R package distributions3. The corresponding PDF slides can be found on my web page https://www.zeileis.org/.


Authors: Achim Zeileis (Universität Innsbruck), Moritz N. Lang (Universität Innsbruck), Alex Hayes (University of Wisconsin-Madison)



Abstract: 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, visualizing their goodness of fit (e.g., via the "topmodels" packages), and assessing their performance (e.g., via the "scoringRules" package).