int3ract is an R package that automates the Johnson-Neyman technique and its three-way JNK extension for interpreting interaction effects in frequentist and Bayesian statistical models.
Venables and Brian D
3 Pith papers cite this work. Polarity classification is still indexing.
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path_boost packages PathBoost, an interpretable path-based gradient booster for graphs that is competitive with GINE and WL+SVR on six molecular regression datasets while exposing which labeled paths drive predictions.
Derives sandwich variance and pairs bootstrap inference for GKRReg coefficients and provides the gkrreg R package with gamma^2 selection tools and datasets.
citing papers explorer
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int3ract: Johnson-Neyman Technique and its Three-Way Extension for Frequentist and Bayesian Models in R
int3ract is an R package that automates the Johnson-Neyman technique and its three-way JNK extension for interpreting interaction effects in frequentist and Bayesian statistical models.
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path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
path_boost packages PathBoost, an interpretable path-based gradient booster for graphs that is competitive with GINE and WL+SVR on six molecular regression datasets while exposing which labeled paths drive predictions.
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Statistical Inference for Gaussian Kernel Robust Regression with the gkrreg Package
Derives sandwich variance and pairs bootstrap inference for GKRReg coefficients and provides the gkrreg R package with gamma^2 selection tools and datasets.