performance: An R Package for Assessment, Comparison and Testing of Statistical Models
By Daniel Lüdecke, Mattan S. Ben-Shachar, Indrajeet Patil, Philip D. Waggoner, Dominique Makowski. Journal of Open Source Software, 2021
4,679 citations recorded by the source corpus.
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. 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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/d639df461a9a67e8a86fb989014fb6f5fd5b60d5
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Citations in the corpus
- Visualizations with statistical details: The 'ggstatsplot' approach (2021, methodology): “Internally, data cleaning is carried out using the tidyverse (Wickham et al., 2019), while statistical analysis is carried out via the statsExpressions (Patil, 2021) and easysta ts (Ben-Shachar et al., 2020; Lüdecke et al., 2019, 2020, 2021; Makowski et al., 2019, 2020) packages.”
- The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux (2024, unknown): “We then estimated the total amount of variance explained by the model including both timescales and derived the unique variance attributable to each timescale separately using the performance library ( 114 ).”
- Distinct functions for beta and alpha bursts in gating of human working memory (2023, unknown): “Thefollowingpackages were used: lme4 70 , lmerTest 71 , Mass 72 , performance 73 .”