Assessing and Visualising the Performance of Prediction Models R package modelskill version 0.1.1 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>. Software Imports: ggplot2 (>= 3.5.0), ggrepel, viridis Suggests: testthat (>= 3.0.0), knitr, rmarkdown, pkgdown Alexandre M.J.-C. Wadoux <alexandre.wadoux@yahoo.fr> Comprehensive R Archive Network (CRAN) Alexandre M.J.-C. Wadoux [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-7325-9716>) MIT + file LICENSE (https://CRAN.R-project.org/package=modelskill/LICENSE) 2026-09-17 application/tgz https://CRAN.R-project.org/package=modelskill doi:10.32614/CRAN.package.modelskill