# easystats/performance

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1,145 stars · 103 forks · R · GPL-3.0

## Links

- GitHub: https://github.com/easystats/performance
- Homepage: https://easystats.github.io/performance/
- awesome-repositories: https://awesome-repositories.com/repository/easystats-performance.md

## Topics

`aic` `easystats` `hacktoberfest` `loo` `machine-learning` `mixed-models` `models` `performance` `r` `r2` `statistics`

## Description

This package is an R library designed as a comprehensive toolkit for statistical model evaluation and validation. It provides a unified framework to assess the quality, predictive accuracy, and underlying assumptions of diverse statistical models, supporting both frequentist and Bayesian paradigms through a consistent interface.

The library distinguishes itself by decoupling diagnostic and performance logic from specific statistical frameworks, allowing for uniform validation across heterogeneous model types. It employs a model-agnostic pipeline that uses generic method dispatch to standardize outputs, enabling direct comparison, ranking, and normalization of metrics across different model structures.

The toolkit covers a broad range of analytical capabilities, including automated diagnostic testing for multicollinearity, outliers, and residual patterns, as well as psychometric scale reliability assessment. It also provides extensive utilities for calculating goodness-of-fit indices, information criteria, and standardized coefficients, complemented by integrated visualization tools for interpreting model performance and diagnostic results.

## Tags

### Programming Languages & Runtimes

- [R](https://awesome-repositories.com/f/programming-languages-runtimes/programming-language-varieties/programming-languages/scientific-data-languages/r.md) — Provides standardized methods to assess model quality, predictive accuracy, and statistical assumptions within the R programming language.
- [Lazy Evaluation](https://awesome-repositories.com/f/programming-languages-runtimes/deferred-execution/lazy-evaluation.md) — Defers heavy statistical computations until requested to optimize performance when analyzing large sets of candidate models.
- [S3 Dispatchers](https://awesome-repositories.com/f/programming-languages-runtimes/programming-utilities/functional/generic-function-definitions/generic-dispatch-mechanisms/s3-dispatchers.md) — Uses language-specific method dispatch to apply consistent statistical calculations across diverse model objects regardless of their underlying implementation.

### Artificial Intelligence & ML

- [Model Agnostic Diagnostics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-agnostic-diagnostics.md) — Decouples diagnostic logic from specific statistical frameworks to enable uniform validation of assumptions across frequentist and Bayesian models.
- [Bayesian Model Comparison](https://awesome-repositories.com/f/artificial-intelligence-ml/model-comparison-tools/bayesian-model-comparison.md) — Ranks and compares multiple statistical models using performance metrics and information criteria to select the best fit for data.
- [Model Performance Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics.md) — Computes standard performance indices such as RMSE, AIC, and BIC to assess predictive accuracy and reliability. ([source](https://github.com/easystats/performance#readme))
- [Comparative Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics/comparative-analyzers.md) — Calculates and visualizes performance indices across several models simultaneously to determine the best fit for data. ([source](https://easystats.github.io/see/articles/performance.html))
- [Statistical Comparison Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics/statistical-comparison-frameworks.md) — Assesses differences between multiple models using likelihood ratio tests and Bayes factors to determine relative quality. ([source](https://easystats.github.io/performance/reference/index.html))
- [Statistical Model Validators](https://awesome-repositories.com/f/artificial-intelligence-ml/statistical-model-validators.md) — Checks statistical assumptions and identifies potential issues like multicollinearity or overdispersion to ensure models are reliable and accurate.
- [Custom Accuracy Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/classifier-accuracy-metrics/custom-accuracy-metrics.md) — Quantifies prediction accuracy using standard error metrics, information criteria, and classification scores. ([source](https://easystats.github.io/performance/reference/index.html))
- [Multicollinearity Analyses](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-correlation-analysis/multicollinearity-analyses.md) — Quantifies multicollinearity within statistical models to identify potential issues with predictor variables. ([source](https://easystats.github.io/blog/posts/))
- [Model Comparison Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/model-comparison-tools.md) — Provides a set of tools for ranking and comparing multiple statistical models using information criteria, Bayes factors, and normalized performance indices.
- [Model Diagnostics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-diagnostics.md) — Identifies statistical violations such as overdispersion, zero-inflation, heteroskedasticity, and singular fits to ensure model assumptions are met. ([source](https://easystats.github.io/performance/))
- [Diagnostic Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-diagnostics/diagnostic-visualizers.md) — Generates graphical representations of model assumptions and performance metrics to facilitate the identification of patterns and structural failures. ([source](https://github.com/easystats/performance#readme))
- [Classification Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics/classification-evaluators.md) — Plots receiver operating characteristic curves to compare the diagnostic ability and predictive accuracy of classification models. ([source](https://easystats.github.io/see/articles/performance.html))
- [Determination Coefficient Calculators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics/determination-coefficient-calculators.md) — Computes coefficients of determination to measure the proportion of variance explained by a model. ([source](https://easystats.github.io/performance/reference/index.html))
- [Performance Rankers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics/performance-rankers.md) — Generates a normalized performance score for each model to provide a clear heuristic ranking of overall model quality. ([source](https://easystats.github.io/performance/reference/compare_performance.html))
- [Performance Summarizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics/performance-summarizers.md) — Computes comprehensive quality metrics and fit indices for diverse statistical models to provide a clear overview of performance. ([source](https://easystats.github.io/performance/))
- [Performance Comparison Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-visualizations/performance-comparison-visualizers.md) — Creates spiderweb plots of normalized performance indices to visually compare the relative strengths and weaknesses of multiple models. ([source](https://easystats.github.io/performance/reference/compare_performance.html))
- [Model Weighting Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-reconstruction/model-weighting-utilities.md) — Computes relative model weights based on information criteria to quantify the evidence supporting each individual model. ([source](https://easystats.github.io/performance/reference/compare_performance.html))
- [Statistical Residual Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/residual-analysis/statistical-residual-analyzers.md) — Visualizes binned residuals to identify patterns or deviations that indicate whether a statistical model accurately represents the underlying distribution of the observed data. ([source](https://easystats.github.io/see/articles/performance.html))

### Scientific & Mathematical Computing

- [Goodness of Fit Metrics](https://awesome-repositories.com/f/scientific-mathematical-computing/goodness-of-fit-metrics.md) — Calculates goodness-of-fit indices, diagnostic metrics, and performance scores for frequentist and Bayesian statistical models.
- [Predictive Accuracy Validators](https://awesome-repositories.com/f/scientific-mathematical-computing/predictive-accuracy-validators.md) — Compares observed data against simulated data from the model to verify if the model successfully captures the variation of the original dataset. ([source](https://easystats.github.io/see/articles/performance.html))
- [Psychometric Reliability Analysis](https://awesome-repositories.com/f/scientific-mathematical-computing/psychometric-reliability-analysis.md) — Provides tools for assessing the internal consistency and reliability of psychometric scales and questionnaire items. ([source](https://easystats.github.io/performance/reference/index.html))
- [Psychometric Reliability Analyzers](https://awesome-repositories.com/f/scientific-mathematical-computing/psychometric-reliability-analyzers.md) — Evaluates the internal consistency and quality of questionnaire items and scales using standard reliability coefficients and correlation analysis.
- [Hypothesis Testing](https://awesome-repositories.com/f/scientific-mathematical-computing/research-analysis-workflows/research-and-data-analysis-tools/statistical-analysis-libraries/hypothesis-testing.md) — Executes hypothesis tests such as Bayes factors and nested model comparisons to determine statistical significance. ([source](https://easystats.github.io/performance/))

### Testing & Quality Assurance

- [Statistical Diagnostics](https://awesome-repositories.com/f/testing-quality-assurance/debugging-diagnostics/diagnostic-tools/statistical-diagnostics.md) — Generates automated visualizations and metrics to detect outliers, residual patterns, and structural failures in various statistical models.
- [Statistical Assumption Validators](https://awesome-repositories.com/f/testing-quality-assurance/assumption-validations/statistical-assumption-validators.md) — Performs diagnostic tests and visual checks to verify statistical assumptions such as normality, homoscedasticity, independence of residuals, and the absence of influential outliers. ([source](https://github.com/easystats/performance#readme))

### System Administration & Monitoring

- [Metric Normalizers](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/unified-metrics-querying/metric-normalizers.md) — Transforms disparate statistical outputs into standardized scales to allow direct comparison and ranking of heterogeneous model types.