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RobustPrediction  

Robust Tuning and Training for Cross-Source Prediction
View on CRAN: Click here


Download and install RobustPrediction package within the R console
Install from CRAN:
install.packages("RobustPrediction")

Install from Github:
library("remotes")
install_github("cran/RobustPrediction")

Install by package version:
library("remotes")
install_version("RobustPrediction", "0.1.7")



Attach the package and use:
library("RobustPrediction")
Maintained by
Yuting He
[Scholar Profile | Author Map]
First Published: 2024-11-14
Latest Update: 2024-11-14
Description:
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>.
How to cite:
Yuting He (2024). RobustPrediction: Robust Tuning and Training for Cross-Source Prediction. R package version 0.1.7, https://cran.r-project.org/web/packages/RobustPrediction. Accessed 17 Feb. 2025.
Previous versions and publish date:
0.1.4 (2024-11-14 14:30)
Other packages that cited RobustPrediction R package
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Other R packages that RobustPrediction depends, imports, suggests or enhances
Complete documentation for RobustPrediction
Functions, R codes and Examples using the RobustPrediction R package
Full RobustPrediction package functions and examples
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