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modelskill  

Assessing and Visualising the Performance of Prediction Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("modelskill", "0.1.1")



Attach the package and use:
library("modelskill")
Maintained by
Alexandre M.J.-C. Wadoux
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-17
Latest Update: 2026-09-17
Description:
Provides tools for evaluating continuous predictions and their associated predictive uncertainty from statistical, machine-learning, geostatistical, and process-based models. It implements complementary measures of prediction error, association, agreement, efficiency, uncertainty calibration, and predictive-distribution performance, together with Taylor, solar, target, coverage, probability integral transform, and quantile-coverage diagnostics. Methods include the integrated evaluation approach of Wadoux, Walvoort and Brus (2022) <doi:10.1016/j.geoderma.2021.115332> and the uncertainty-validation framework of Schmidinger and Heuvelink (2023) <doi:10.1016/j.geoderma.2023.116585>.
How to cite:
Alexandre M.J.-C. Wadoux (2026). modelskill: Assessing and Visualising the Performance of Prediction Models. R package version 0.1.1, https://cran.r-project.org/web/packages/modelskill. Accessed 24 Oct. 2026.
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