performance-package: performance: An R Package for Assessment, Comparison and...

In performance: Assessment of Regression Models Performance

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

Description

A crucial aspect when building regression models is to evaluate the quality of modelfit. It is important to investigate how well models fit to the data and which fit indices to report. Functions to create diagnostic plots or to compute fit measures do exist, however, mostly spread over different packages. There is no unique and consistent approach to assess the model quality for different kind of models.

The primary goal of the performance package is to fill this gap and to provide utilities for computing indices of model quality and goodness of fit. These include measures like r-squared (R2), root mean squared error (RMSE) or intraclass correlation coefficient (ICC), but also functions to check (mixed) models for overdispersion, zero-inflation, convergence or singularity.

References: Lüdecke et al. (2021) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.21105/joss.03139")}

Details

performance-package

Author(s)

Maintainer: Daniel Lüdecke [e-mail] (ORCID) (@strengejacke)

Authors:

• Dominique Makowski [e-mail] (ORCID) (@Dom_Makowski) [contributor]

• Mattan S. Ben-Shachar [e-mail] (ORCID) (@mattansb) [contributor]

• Indrajeet Patil [e-mail] (ORCID) (@patilindrajeets) [contributor]

• Philip Waggoner [e-mail] (ORCID) [contributor]

• Brenton M. Wiernik [e-mail] (ORCID) (@bmwiernik) [contributor]

• Rémi Thériault [e-mail] (ORCID) (@rempsyc) [contributor]

Other contributors:

• Vincent Arel-Bundock [e-mail] (ORCID) [contributor]

• Martin Jullum [reviewer]

• gjo11 [reviewer]

• Etienne Bacher [e-mail] (ORCID) [contributor]

See Also

Useful links:

• https://easystats.github.io/performance/

• Report bugs at https://github.com/easystats/performance/issues

performance documentation built on June 22, 2024, 10:55 a.m.