performance: An R Package for Assessment, Comparison and Testing of Statistical Models

Abstract

A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models. Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort. The performance R-package (R Core Team, 2021) provides utilities for computing measures to assess model quality, many of which are not directly provided by R's base or stats packages. These include measures like R 2 , intraclass correlation coefficient (ICC), root mean squared error (RMSE), or functions to check for vexing issues like overdispersion, singularity, or zero-inflation. These functions support a large variety of regression models including generalized linear models, (generalized) mixed-effects models, their Bayesian cousins, and many others. Statement of Need While functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models. The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes. The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations. performance is part of the easystats ecosystem, which is a collaborative project focused on facilitating simple and intuitive usage of R for statistical analysis (Ben-Shachar et al.,