RobustPrediction

Provides robust parameter tuning and model training for predictive models across data sources. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" doi:10.1007/s00357-020-09368-z.

Last 30 days

This package has been downloaded 129 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,221 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Dec 17, 2024 with 46 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.