pdp - Partial Dependence Plots
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types of machine learning models in R.
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black-box-modelmachine-learningpartial-dependence-functionpartial-dependence-plotvisualization
12.35 score 99 stars 6 dependents 2.0k scripts 7.1k downloadsvip - Variable Importance Plots
A general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <doi:10.48550/arXiv.1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).
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interaction-effectmachine-learningpartial-dependence-plotsupervised-learning-algorithmsvariable-importancevariable-importance-plots
10.08 score 193 stars 2 dependents 5.2k scripts 759 downloadsinvestr - Inverse Estimation/Calibration Functions
Functions to facilitate inverse estimation (e.g., calibration) in linear, generalized linear, nonlinear, and (linear) mixed-effects models. A generic function is also provided for plotting fitted regression models with or without confidence/prediction bands that may be of use to the general user. For a general overview of these methods, see Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.
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calibrationinverse-estimationinverse-predictionregression
9.15 score 27 stars 2 dependents 189 scripts 15k downloadsfastshap - Fast Approximate Shapley Values
Computes fast (relative to other implementations) approximate Shapley values for any supervised learning model. Shapley values help to explain the predictions from any black box model using ideas from game theory; see Strumbelj and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> for details.
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explainable-aiexplainable-mlinterpretable-machine-learningshapleyshapley-valuesvariable-importancexaicpp
7.44 score 137 stars 448 scripts 597 downloadssure - Surrogate Residuals for Ordinal and General Regression Models
An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2018) <doi:10.1080/01621459.2017.1292915> and Greenwell et al. (2018) <doi:10.32614/RJ-2018-004>. These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides 'plot' methods for producing standard diagnostic plots using lightweight base R graphics (via 'tinyplot'). The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM', binary and other generalized linear models fit with 'glm', and multinomial models or general classifiers via a matrix of predicted class probabilities.
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categorical-datadiagnosticsordinal-regressionresiduals
6.91 score 8 stars 1 dependents 76 scripts 567 downloads
statlingo - Explain Statistical Output with Large Language Models
Transform complex statistical output into straightforward, understandable, and context-aware natural language descriptions using Large Language Models (LLMs), making complex analyses more accessible to individuals with varying statistical expertise. It relies on the 'ellmer' package to interface with LLM providers including OpenAI <https://openai.com/>, Google AI Studio <https://aistudio.google.com/>, and Anthropic <https://www.anthropic.com/> (API keys are required and managed via 'ellmer').
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data-scienceexplainabilitylarge-language-modelsllmllmsstatisticsteaching-tools
4.22 score 11 stars 6 scripts