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 zeroinflation.These functions support a large variety of regression models including generalized linear models, (generalized) mixed-effects models, their Bayesian cousins, and many others.

Keywords

R packageComputer scienceStatistical hypothesis testingReliability engineeringStatisticsMathematicsEngineeringProgramming language

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Publication Info

Year
2021
Type
article
Volume
6
Issue
60
Pages
3139-3139
Citations
4251
Access
Closed

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Cite This

Daniel Lüdecke, Mattan S. Ben‐Shachar, Indrajeet Patil et al. (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. The Journal of Open Source Software , 6 (60) , 3139-3139. https://doi.org/10.21105/joss.03139

Identifiers

DOI
10.21105/joss.03139