Published November 28, 2025 | Version v1.0.2

PetitPascal/R-scripts: Collection of R scripts for reproducible data analysis

Authors/Creators

  • 1. Universite Grenoble Alpes

Description

This release includes updates and additions to the R scripts repository for reproducible data analysis.

Release notes (v1.0.2)

This release includes updates and additions to the R scripts repository for reproducible data analysis. Changes in this version include:

New scripts added

XGBoost_linear regression_nested CV.R: Extreme Gradient Boosting (XGBoost) for linear regression with nested cross-validation.

XGBoost_ordinal logistic classification.R: XGBoost for logistic ordinal regression with nested cross-validation.

Multi-class XGBoost.R: multi-class prediction using XGBoost.

Continuous_Multioutput_XGBoost.R: generalizable R and Python workflow for continuous multi-output regression using XGBoost.

Linear BMA.R: Bayesian Model Averaging (BMA) analysis for probabilistic model selection in linear regression.

SHAP_direction.R: function for determining feature direction based on SHAP values using Rcpp pairwise counting.

BKMR.R: Bayesian kernel machine regression (BKMR) analysis.

WQS.R: weighted quantile sum regression (WQS) analysis.

PSM.R: propensity score matching (PSM) analysis.

RCS.R: restricted cubic spline (RCS) analysis.

Convert_semi transparent color to opaque.R: function to convert a semi-transparent color to its opaque equivalent, considering the visual effect on a white background.

Loading and using a model.R: function for loading a saved model and make new prediction.

Carbon_footprint.R: function attempting to estimate energy consumption and carbon emissions from running R code.

Updated scripts

Logistic BMA.R: added example dataset for reproducibility.

Survival XGBoost Cox analysis.R: added example dataset for reproducibility.

XGBoost_binary classification_nested CV.R: added example dataset for reproducibility.

Usage notes

• All scripts are documented with instructions and, where applicable, example datasets.

• Ensure the required R packages are installed: xgboost, BMA, survival, etc.

Version notes

• This release is version v1.0.2.

• For reproducibility, cite the DOI of this release when using these scripts.

All previously released scripts are included unchanged in this version.

Files

PetitPascal/R-scripts-v1.0.2.zip

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