performance: Assessment of Regression Models Performance

Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models.

Version: 0.1.0
Imports: insight, bayestestR
Suggests: brms, covr, graphics, glmmTMB, ggplot2, lme4, loo, Matrix, MASS, mlogit, nlme, psych, rmarkdown, rstanarm, rstantools, scales, testthat
Published: 2019-04-24
Author: Daniel Lüdecke ORCID iD [aut, cre], Dominique Makowski ORCID iD [aut, ctb]
Maintainer: Daniel Lüdecke <d.luedecke at uke.de>
BugReports: https://github.com/easystats/performance/issues
License: GPL-3
URL: https://easystats.github.io/performance/
NeedsCompilation: no
Materials: README
CRAN checks: performance results

Downloads:

Reference manual: performance.pdf
Package source: performance_0.1.0.tar.gz
Windows binaries: r-devel: performance_0.1.0.zip, r-release: performance_0.1.0.zip, r-oldrel: performance_0.1.0.zip
OS X binaries: r-release: performance_0.1.0.tgz, r-oldrel: performance_0.1.0.tgz

Reverse dependencies:

Reverse imports: sjPlot

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