{ "_id": "6ab0be923d719c1f8408323c", "Package": "modelskill", "Type": "Package", "Title": "Assessing and Visualising the Performance of Prediction Models", "Version": "0.1.1", "Authors@R": "c(\nperson(\n\"Alexandre M.J.-C.\", \"Wadoux\",\nemail = \"alexandre.wadoux@yahoo.fr\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-7325-9716\")\n)\n)", "Description": "Provides tools for evaluating continuous predictions and\ntheir associated predictive uncertainty from statistical,\nmachine-learning, geostatistical, and process-based models. It\nimplements complementary measures of prediction error,\nassociation, agreement, efficiency, uncertainty calibration,\nand predictive-distribution performance, together with Taylor,\nsolar, target, coverage, probability integral transform, and\nquantile-coverage diagnostics. Methods include the integrated\nevaluation approach of Wadoux, Walvoort and Brus (2022)\n and the\nuncertainty-validation framework of Schmidinger and Heuvelink\n(2023) .", "License": "MIT + file LICENSE", "URL": "https://github.com/AlexandreWadoux/modelskill,\nhttps://alexandrewadoux.github.io/modelskill/", "BugReports": "https://github.com/AlexandreWadoux/modelskill/issues", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.0", "VignetteBuilder": "knitr", "Repository": "https://alexandrewadoux.r-universe.dev", "Date/Publication": "2026-09-21 02:19:02 UTC", "RemoteUrl": "https://github.com/alexandrewadoux/modelskill", "RemoteRef": "HEAD", "RemoteSha": "6bcd03833b0eb95fb717f9a073247308f3be2df7", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-21 05:14:49 UTC", "User": "root" }, "Author": "Alexandre M.J.-C. Wadoux [aut, cre, cph] (ORCID:\n)", "Maintainer": "Alexandre M.J.-C. 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correlation", "topics": [ "correlation" ] }, { "page": "coverage", "title": "Empirical prediction-interval coverage", "topics": [ "coverage" ] }, { "page": "coverage_error", "title": "Prediction-interval coverage error", "topics": [ "coverage_error" ] }, { "page": "crmse", "title": "Centered root mean squared error", "topics": [ "crmse" ] }, { "page": "crps", "title": "Continuous ranked probability score", "topics": [ "crps" ] }, { "page": "crps_decomposition", "title": "CRPS reliability decomposition", "topics": [ "crps_decomposition" ] }, { "page": "diagram_stats", "title": "Statistics underlying summary diagrams", "topics": [ "diagram_stats" ] }, { "page": "gg_coverage", "title": "Plot prediction-interval calibration", "topics": [ "gg_coverage" ] }, { "page": "gg_pit", "title": "Plot a probability integral transform histogram", "topics": [ "gg_pit" ] }, { "page": "gg_qcp", "title": "Quantile calibration plot", "topics": [ "gg_qcp" ] }, { "page": "gg_solar", "title": "Create a solar diagram", "topics": [ "gg_solar" ] }, { "page": "gg_target", "title": "Create a target diagram", "topics": [ "gg_target" ] }, { "page": "gg_taylor", "title": "Create a Taylor diagram", "topics": [ "gg_taylor" ] }, { "page": "interval_score", "title": "Central prediction-interval score", "topics": [ "interval_score" ] }, { "page": "interval_width", "title": "Average prediction-interval width", "topics": [ "interval_width" ] }, { "page": "kge", "title": "Kling-Gupta efficiency: KGE (2009)", "topics": [ "kge" ] }, { "page": "log_score", "title": "Logarithmic (ignorance) score", "topics": [ "log_score" ] }, { "page": "mae", "title": "Mean absolute error", "topics": [ "mae" ] }, { "page": "mape", "title": "Mean absolute percentage error", "concept": [ "prediction metrics" ], "topics": [ "mape" ] }, { "page": "mdae", "title": "Median absolute error", "concept": [ "prediction metrics" ], "topics": [ "mdae" ] }, { "page": "mec", "title": "Model efficiency coefficient", "topics": [ "mec" ] }, { "page": "median_crps", "title": "Median continuous ranked probability score", "topics": [ "median_crps" ] }, { "page": "model_metrics", "title": "Calculate prediction-validation metrics for one or more models", "topics": [ "model_metrics" ] }, { "page": "mpe", "title": "Mean percentage error", "concept": [ "prediction metrics" ], "topics": [ "mpe" ] }, { "page": "mse", "title": "Mean squared error", "topics": [ "mse" ] }, { "page": "msle", "title": "Mean squared logarithmic error", "concept": [ "prediction metrics" ], "topics": [ "msle" ] }, { "page": "nrmse", "title": "Normalized root mean squared error", "topics": [ "nrmse" ] }, { "page": "nse", "title": "Nash-Sutcliffe efficiency", "topics": [ "nse" ] }, { "page": "picp", "title": "Prediction interval coverage probability (PICP)", "topics": [ "picp" ] }, { "page": "pinball_loss", "title": "Quantile (pinball) loss", "topics": [ "pinball_loss" ] }, { "page": "pit", "title": "Probability integral transform", "topics": [ "pit" ] }, { "page": "qcp", "title": "Quantile coverage probability", "topics": [ "qcp" ] }, { "page": "r2", "title": "Squared Pearson correlation", "topics": [ "r2" ] }, { "page": "efficiency_r2", "title": "Model-efficiency R-squared", "topics": [ "R2" ] }, { "page": "rae", "title": "Relative absolute error", "concept": [ "prediction metrics" ], "topics": [ "rae" ] }, { "page": "rer", "title": "Range-to-RMSE ratio", "concept": [ "prediction metrics" ], "topics": [ "rer" ] }, { "page": "rmse", "title": "Root mean squared error", "topics": [ "rmse" ] }, { "page": "rmsle", "title": "Root mean squared logarithmic error", "concept": [ "prediction metrics" ], "topics": [ "rmsle" ] }, { "page": "rpd", "title": "Ratio of performance to deviation", "concept": [ "prediction metrics" ], "topics": [ "rpd" ] }, { "page": "rpiq", "title": "Ratio of performance to interquartile distance", "concept": [ "prediction metrics" ], "topics": [ "rpiq" ] }, { "page": "rrmse", "title": "Relative root mean squared error", 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"RColorBrewer", "Rcpp", "rlang", "S7", "scales", "vctrs", "viridis", "viridisLite", "withr" ], "_vignettes": [ { "source": "performance-metrics.Rmd", "filename": "performance-metrics.html", "title": "Prediction performance metrics", "author": "Alexandre M.J.-C. Wadoux", "engine": "knitr::rmarkdown", "headings": [ "1. Why use several prediction metrics?", "2. Example data", "3. Start by looking at the predictions", "4. Calculate individual metrics", "4.1 RMSE: overall error magnitude", "[\\mathrm", "4.2 Bias: systematic overprediction or underprediction", "[\\mathrm", "5. A particularly important distinction: r2() versus R2()", "5.1 Lowercase r2()", "[r^2", "5.2 Uppercase R2()", "[R^2", "5.3 Correlation is not agreement", "6. Compare several models at once", "7. Categories of prediction performance metrics", "8. Core metrics", "9. Extended metrics", "9.1 Extended metric reference", "10. Quantile predictions", "11. How should metrics be combined?", "12. Related and redundant metrics", "[\\mathrm", "13. Missing values and special cases", "14. Practical recommendations", "15. References", "16. Next steps" ], "created": "2026-09-08 02:21:17", "modified": "2026-09-09 05:26:20", "commits": 13 }, { "source": "predictive-uncertainty.Rmd", "filename": "predictive-uncertainty.html", "title": "Predictive-uncertainty evaluation", "author": "Alexandre M.J.-C. Wadoux", "engine": "knitr::rmarkdown", "headings": [ "1. Why validate predictive uncertainty?", "2. Which form of predictive uncertainty do you have?", "3. Functions used in this tutorial", "4. Example data", "5. Case A: predictive mean and standard deviation", "5.1 Prediction intervals", "5.1.1 Prediction interval coverage probability", "5.1.2 Coverage error", "5.1.3 Prediction interval width", "5.1.4 Interval score", "5.1.5 Calculate all interval diagnostics together", "5.2 Why calibration and sharpness must be considered together", "5.3 Reliability across multiple interval levels", "5.3.1 Numerical summaries of the reliability plot", "5.4 Why PICP alone is not enough", "5.5 Quantile coverage probability", "5.6 Probability integral transform", "5.7 Proper scoring rules", "5.7.1 Continuous ranked probability score", "5.7.2 Median CRPS", "5.7.3 Logarithmic score", "6. Case B: predictive distributions represented by samples", "6.1 Prediction intervals from predictive samples", "6.2 Reliability across interval levels from predictive samples", "6.3 Quantile calibration from predictive samples", "6.4 PIT from predictive samples", "6.5 CRPS from predictive samples", "6.6 CRPS reliability decomposition", "6.7 What about the logarithmic score with predictive samples?", "7. Comparing the two input formats", "8. A practical workflow", "8.1 If you only have prediction intervals", "8.2 If you have predicted quantiles", "8.3 If you have a predictive mean and standard deviation", "8.4 If you have predictive samples", "9. Recommended interpretation" ], "created": "2026-09-07 07:03:53", "modified": "2026-09-08 23:59:16", "commits": 13 }, { "source": "summary-diagrams.Rmd", "filename": "summary-diagrams.html", "title": "Summary diagrams and diagnostic plots", "author": "Alexandre M.J.-C. Wadoux", "engine": "knitr::rmarkdown", "headings": [ "1. Why use summary diagrams?", "2. Example data", "3. Solar diagram", "[\\mathrm", "[\\mathrm", "[\\mathrm", "[\\mathrm", "[\\mathrm", "[\\mathrm", "3.1 Reading a solar diagram", "[\\mathrm", "3.2 Basic solar diagram", "3.3 Colour models instead of writing labels", "3.4 Control the reference geometry", "4. Target diagram", "4.1 Reading a target diagram", "4.1.1 Note on the modelskill coordinate convention", "4.2 Basic target diagram", "4.3 Colour models with a legend", "4.4 Change the displayed range", "5. Taylor diagram", "[\\sigma^", "5.1 The reference point", "5.2 Full Taylor diagram", "5.3 Half Taylor diagram", "5.4 Labels or legend", "5.5 Centred RMSD contours", "5.6 A limitation of the Taylor diagram", "6. Which diagram should I use?", "7. Customising the diagrams with ggplot2", "7.1 Add titles and change the theme", "7.2 Move the legend", "7.3 Change the model colour palette", "7.4 Change the target axis titles", "7.5 Zoom without changing the statistics", "7.6 Change point and label sizes", "8. 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