vip - 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
12.29 score 190 stars 2 dependents 4.8k scripts 18k downloadspdp - Partial Dependence Plots
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
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black-box-modelmachine-learningpartial-dependence-functionpartial-dependence-plotvisualization
11.71 score 99 stars 3 dependents 1.6k scripts 12k 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 Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> for details.
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explainable-aiexplainable-mlinterpretable-machine-learningshapleyshapley-valuesvariable-importancexaicpp
9.63 score 133 stars 2 dependents 422 scripts 8.4k 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
8.60 score 25 stars 2 dependents 200 scripts 27k downloadsramify - Additional Matrix Functionality
Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or 'Python'+'NumPy'.
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matrices
6.63 score 3 stars 4 dependents 237 scripts 345 downloads
statlingua - 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
5.48 score 10 stars 12 scripts 190 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 (2017, <doi:https://doi.org/10.1080/01621459.2017.1292915>) and Greenwell et al. (2017, <https://journal.r-project.org/archive/2018/RJ-2018-004/index.html>). 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 an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available.
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categorical-datadiagnosticsordinal-regressionresiduals
5.43 score 8 stars 1 dependents 75 scripts 756 downloadsebm - Explainable Boosting Machines
An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) <doi:10.48550/arXiv.1909.09223> for. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.
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aiblackboxexplainable-aiexplainable-machine-learningexplainable-mlglassboximlinterpretabilityinterpretability-and-explainabilityinterpretableinterpretable-aiinterpretable-machine-learninginterpretable-mlinterpretable-modelsmachine-learningxai
4.85 score 5 stars 14 scripts 130 downloadsbpa - Basic Pattern Analysis
Run basic pattern analyses on character sets, digits, or combined input containing both characters and numeric digits. Useful for data cleaning and for identifying columns containing multiple or nonstandard formats.
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basic-pattern-analysisdata-cleaningstandardization
4.45 score 4 stars 14 scripts 245 downloadsroundhouse - Random Chuck Norris Facts
R functions for generating and/or displaying random Chuck Norris facts. Based on data from the 'Internet Chuck Norris database' ('ICNDb').
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apiapi-wrapperchuck-norrischuck-norris-jokesfactsroundhouse
3.18 score 3 stars 4 scripts 228 downloads