{ "_id": "6a1f124eb401979e7341dd06", "Package": "auditor", "Title": "Model Audit - Verification, Validation, and Error Analysis", "Version": "1.3.5", "Authors@R": "c(\nperson(\"Alicja\", \"Gosiewska\", email = \"alicjagosiewska@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-6563-5742\")),\nperson(\"Przemyslaw\", \"Biecek\", role = c(\"aut\", \"ths\"),\ncomment = c(ORCID = \"0000-0001-8423-1823\")),\nperson(\"Hubert\", \"Baniecki\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-6661-5364\")),\nperson(\"Tomasz\", \"Mikołajczyk\", role = c(\"aut\")),\nperson(\"Michal\", \"Burdukiewicz\", role = c(\"ctb\")),\nperson(\"Szymon\", \"Maksymiuk\", role = c(\"ctb\"))\n)", "Description": "Provides an easy to use unified interface for creating\nvalidation plots for any model. The 'auditor' helps to avoid\nrepetitive work consisting of writing code needed to create\nresidual plots. This visualizations allow to asses and compare\nthe goodness of fit, performance, and similarity of models.", "License": "GPL", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "URL": "https://github.com/ModelOriented/auditor", "BugReports": "https://github.com/ModelOriented/auditor/issues", "Language": "en-US", "Repository": "https://modeloriented.r-universe.dev", "Date/Publication": "2023-10-30 15:18:20 UTC", "RemoteUrl": "https://github.com/modeloriented/auditor", "RemoteRef": "HEAD", "RemoteSha": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-17 07:41:55 UTC", "User": "root" }, "Author": "Alicja Gosiewska [aut, cre] (ORCID:\n),\nPrzemyslaw Biecek [aut, ths] (ORCID:\n),\nHubert Baniecki [aut] (ORCID: ),\nTomasz Mikołajczyk [aut],\nMichal Burdukiewicz [ctb],\nSzymon Maksymiuk [ctb]", "Maintainer": "Alicja Gosiewska ", "MD5sum": "2b7f4e00389bb78cbe5cbbdbb4d93916", "_user": "modeloriented", "_type": "src", "_file": "auditor_1.3.5.tar.gz", "_fileid": "8ba3d1150caf715e26c3e11f44153227804e8aa0fa3fca7d12b928ee9e429c22", "_filesize": 1686737, "_sha256": "8ba3d1150caf715e26c3e11f44153227804e8aa0fa3fca7d12b928ee9e429c22", "_created": "2026-05-17T07:41:55.000Z", "_published": "2026-06-02T17:26:38.432Z", "_distro": "noble", "_jobs": [ { "job": 79131572858, "time": 203, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7040535017" }, { "job": 79131573431, "time": 198, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7040534472" }, { "job": 79131573083, "time": 158, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7040530015" }, { "job": 79131573769, "time": 122, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7040526050" }, { "job": 79131572489, "time": 241, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7040512074" }, { "job": 79131572348, "time": 114, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7365060484" }, { "job": 79131573285, "time": 179, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7040532301" }, { "job": 79131573213, "time": 157, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7040529997" }, { "job": 79131573498, "time": 171, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7040531414" } ], "_buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/modeloriented/auditor", "_commit": { "id": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "author": "Alicja Gosiewska ", "committer": "GitHub ", "message": "Merge pull request #162 from michbur/master\n\nReady for the cran submission", "time": 1698679100 }, "_maintainer": { "name": "Alicja Gosiewska", "email": "alicjagosiewska@gmail.com", "login": "agosiewska", "twitter": "@alicjagosiewska", "description": "Senior Machine Learning Research Engineer with 5+ experience in data science. Skilled in Python and R.", "uuid": 18423383, "orcid": "0000-0001-6563-5742" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "DALEX", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "ggrepel", "role": "Imports" }, { "package": "grid", "role": "Imports" }, { "package": "gridExtra", "role": "Imports" }, { "package": "hnp", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "jsonlite", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "markdown", "role": "Suggests" }, { "package": "mgcv", "role": "Suggests" }, { "package": "r2d3", "role": "Suggests" }, { "package": "randomForest", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "spelling", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "covr", "role": "Suggests" } ], "_owner": "modeloriented", "_selfowned": true, "_usedby": 2, "_updates": [], "_tags": [], "_topics": [ "classification", "error-analysis", "explainable-artificial-intelligence", "machine-learning", "model-validation", "regression-models", "residuals", "xai" ], "_stars": 59, "_contributors": [ { "user": "agosiewska", "count": 363, "uuid": 18423383 }, { "user": "tmikolajczyk", "count": 74, "uuid": 10706421 }, { "user": "hbaniecki", "count": 24, "uuid": 32574004 }, { "user": "byrolew", "count": 13, "uuid": 17530886 }, { "user": "maksymiuks", "count": 8, "uuid": 32574056 }, { "user": "pbiecek", "count": 8, "uuid": 4624318 }, { "user": "michbur", "count": 4, "uuid": 5267317 }, { "user": "mstaniak", "count": 1, "uuid": 20418265 } ], "_userbio": { "uuid": 44725555, "type": "organization", "name": "Model Oriented" }, "_downloads": { "count": 3689, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/auditor" }, "_devurl": "https://github.com/modeloriented/auditor", "_searchresults": 100, "_rbuild": "4.6.0", "_assets": [ "extra/auditor.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/modeloriented/auditor", "_realowner": "modeloriented", "_cranurl": true, "_releases": [ { "version": "0.2.0", "date": "2018-05-11" }, { "version": "0.2.1", "date": "2018-05-13" }, { "version": "0.3.0", "date": "2018-08-27" }, { "version": "0.3.1", "date": "2018-09-19" }, { "version": "1.0.0", "date": "2019-08-31" }, { "version": "1.1.0", "date": "2019-09-24" }, { "version": "1.2.0", "date": "2020-02-18" }, { "version": "1.3.0", "date": "2020-05-28" }, { "version": "1.3.3", "date": "2021-07-26" }, { "version": "1.3.5", "date": "2023-10-30" } ], "_exports": [ "audit", "check_residuals", "check_residuals_autocorrelation", "check_residuals_outliers", "check_residuals_trend", "model_cooksdistance", "model_evaluation", "model_halfnormal", "model_performance", "model_residual", "modelEvaluation", "modelFit", "modelPerformance", "modelResiduals", "observationInfluence", "plot_acf", "plot_auditor", "plot_autocorrelation", "plot_cooksdistance", "plot_correlation", "plot_halfnormal", "plot_lift", "plot_pca", "plot_prc", "plot_prediction", "plot_radar", "plot_rec", "plot_residual", "plot_residual_boxplot", "plot_residual_density", "plot_roc", "plot_rroc", "plot_scalelocation", "plot_tsecdf", "plotACF", "plotAutocorrelation", "plotCooksDistance", "plotD3", "plotD3_acf", "plotD3_autocorrelation", "plotD3_cooksdistance", "plotD3_halfnormal", "plotD3_lift", "plotD3_prediction", "plotD3_rec", "plotD3_residual", "plotD3_roc", "plotD3_rroc", "plotD3_scalelocation", "plotD3ACF", "plotD3Autocorrelation", "plotD3CooksDistance", "plotD3HalfNormal", "plotD3LIFT", "plotD3Prediction", "plotD3REC", "plotD3Residual", "plotD3ScaleLocation", "plotHalfNormal", "plotLIFT", "plotModelCorrelation", "plotModelPCA", "plotModelRanking", "plotPrediction", "plotREC", "plotResidual", "plotResidualBoxplot", "plotResidualDensity", "plotROC", "plotRROC", "plotScaleLocation", "plotTwoSidedECDF", "score", "score_acc", "score_auc", "score_auprc", "score_cooksdistance", "score_dw", "score_f1", "score_gini", "score_halfnormal", "score_mae", "score_mse", "score_one_minus_acc", "score_one_minus_auc", "score_one_minus_auprc", "score_one_minus_f1", "score_one_minus_gini", "score_one_minus_precision", "score_one_minus_recall", "score_one_minus_specificity", "score_peak", "score_precision", "score_r2", "score_rec", "score_recall", "score_rmse", "score_rroc", "score_runs", "score_specificity", "scoreCooksDistance", "scoreDW", "scoreHalfNormal", "scoreMAE", "scoreMSE", "scorePeak", "scoreREC", "scoreRMSE", "scoreROC", "scoreRROC", "scoreRuns" ], "_datasets": [ { "name": "auditorData", "title": "Artificial auditorData", "object": "auditorData", "class": [ "data.frame" ], "fields": [ "y", "X1", "X2", "X3", "X4" ], "rows": 2000, "table": true, "tojson": true } ], "_help": [ { "page": "audit", "title": "Deprecated", "topics": [ "audit" ] }, { "page": "auditorData", "title": "Artificial auditorData", "topics": [ "auditorData" ] }, { "page": "check_residuals", "title": "Automated tests for model residuals", "topics": [ "check_residuals" ] }, { "page": "check_residuals_autocorrelation", "title": "Checks for autocorrelation in target variable or in residuals", "topics": [ "check_residuals_autocorrelation" ] }, { "page": "check_residuals_outliers", "title": "Checks for outliers", "topics": [ "check_residuals_outliers" ] }, { "page": "check_residuals_trend", "title": "Checks for trend in residuals", "topics": [ "check_residuals_trend" ] }, { "page": "model_cooksdistance", "title": "Cook's distances", "topics": [ "model_cooksdistance", "observationInfluence" ] }, { "page": "model_evaluation", "title": "Create model evaluation explanation", "topics": [ "modelEvaluation", "model_evaluation" ] }, { "page": "model_halfnormal", "title": "Create Halfnormal Explanation", "topics": [ "modelFit", "model_halfnormal" ] }, { "page": "model_performance", "title": "Create Model Performance Explanation", "topics": [ "modelPerformance", "model_performance" ] }, { "page": "model_residual", "title": "Create Model Residuals Explanation", "topics": [ "modelResiduals", "model_residual" ] }, { "page": "plot_acf", "title": "Autocorrelation Function Plot", "topics": [ "plotACF", "plot_acf" ] }, { "page": "plot", "title": "Model Diagnostic Plots", "topics": [ "plot.auditor_model_cooksdistance", "plot.auditor_model_evaluation", "plot.auditor_model_halfnormal", "plot.auditor_model_performance", "plot.auditor_model_residual", "plot_auditor" ] }, { "page": "plot_autocorrelation", "title": "Autocorrelation of Residuals Plot", "topics": [ "plotAutocorrelation", "plot_autocorrelation" ] }, { "page": "plot_cooksdistance", "title": "Influence of Observations Plot", "topics": [ "plotCooksDistance", "plot_cooksdistance" ] }, { "page": "plot_correlation", "title": "Correlation of Model's Residuals Plot", "topics": [ "plotModelCorrelation", "plot_correlation" ] }, { "page": "plot_halfnormal", "title": "Half-Normal plot", "topics": [ "plotHalfNormal", "plot_halfnormal" ] }, { "page": "plot_lift", "title": "LIFT Chart", "topics": [ "plotLIFT", "plot_lift" ] }, { "page": "plot_pca", "title": "Principal Component Analysis of models", "topics": [ "plotModelPCA", "plot_pca" ] }, { "page": "plot_roc", "title": "Precision-Recall Curve (PRC)", "topics": [ "plotROC", "plot_prc", "plot_roc" ] }, { "page": "plot_prediction", "title": "Predicted response vs Observed or Variable Values", "topics": [ "plotPrediction", "plot_prediction" ] }, { "page": "plot_radar", "title": "Model Ranking Plot", "topics": [ "plotModelRanking", "plot_radar" ] }, { "page": "plot_rec", "title": "Regression Error Characteristic Curves (REC)", "topics": [ "plotREC", "plot_rec" ] }, { "page": "plot_residual", "title": "Plot Residuals vs Observed, Fitted or Variable Values", "topics": [ "plotResidual", "plot_residual" ] }, { "page": "plot_residual_boxplot", "title": "Plot Boxplots of Residuals", "topics": [ "plotResidualBoxplot", "plot_residual_boxplot" ] }, { "page": "plot_residual_density", "title": "Residual Density Plot", "topics": [ "plotResidualDensity", "plot_residual_density" ] }, { "page": "plot_rroc", "title": "Regression Receiver Operating Characteristic (RROC)", "topics": [ "plotRROC", "plot_rroc" ] }, { "page": "plot_scalelocation", "title": "Scale location plot", "topics": [ "plotScaleLocation", "plot_scalelocation" ] }, { "page": "plot_tsecdf", "title": "Two-sided Cumulative Distribution Function", "topics": [ "plotTwoSidedECDF", "plot_tsecdf" ] }, { "page": "plotD3", "title": "Model Diagnostic Plots in D3 with r2d3 package.", "topics": [ "plotD3", "plotD3.auditor_model_cooksdistance", "plotD3.auditor_model_evaluation", "plotD3.auditor_model_halfnormal", "plotD3.auditor_model_residual", "plotD3_auditor" ] }, { "page": "plotD3_acf", "title": "Plot Autocorrelation Function in D3 with r2d3 package.", "topics": [ "plotD3ACF", "plotD3_acf" ] }, { "page": "plotD3_autocorrelation", "title": "Autocorrelation Plot in D3 with r2d3 package.", "topics": [ "plotD3Autocorrelation", "plotD3_autocorrelation" ] }, { "page": "plotD3_cooksdistance", "title": "Influence of observations Plot in D3 with r2d3 package.", "topics": [ "plotD3CooksDistance", "plotD3_cooksdistance" ] }, { "page": "plotD3_halfnormal", "title": "Plot Half-Normal in D3 with r2d3 package.", "topics": [ "plotD3HalfNormal", "plotD3_halfnormal" ] }, { "page": "plotD3_lift", "title": "Plot LIFT in D3 with r2d3 package.", "topics": [ "plotD3LIFT", "plotD3_lift" ] }, { "page": "plotD3_prediction", "title": "Plot Prediction vs Target, Observed or Variable Values in D3 with r2d3 package.", "topics": [ "plotD3Prediction", "plotD3_prediction" ] }, { "page": "plotD3_rec", "title": "Regression Error Characteristic Curves (REC) in D3 with r2d3 package.", "topics": [ "plotD3REC", "plotD3_rec" ] }, { "page": "plotD3_residual", "title": "Plot Residuals vs Observed, Fitted or Variable Values in D3 with r2d3 package.", "topics": [ "plotD3Residual", "plotD3_residual" ] }, { "page": "plotD3_roc", "title": "Receiver Operating Characteristic (ROC) in D3 with r2d3 package.", "topics": [ "plotD3_roc" ] }, { "page": "plotD3_rroc", "title": "Regression Receiver Operating Characteristic (RROC) in D3 with r2d3 package.", "topics": [ "plotD3_rroc" ] }, { "page": "plotD3_scalelocation", "title": "Scale Location Plot in D3 with r2d3 package.", "topics": [ "plotD3ScaleLocation", "plotD3_scalelocation" ] }, { "page": "print.auditor_model_cooksdistance", "title": "Prints Model Cook's Distances Summary", "topics": [ "print.auditor_model_cooksdistance" ] }, { "page": "print.auditor_model_evaluation", "title": "Prints Model Evaluation Summary", "topics": [ "print.auditor_model_evaluation" ] }, { "page": "print.auditor_model_halfnormal", "title": "Prints Model Halfnormal Summary", "topics": [ "print.auditor_model_halfnormal" ] }, { "page": "print.auditor_model_performance", "title": "Prints Model Performance Summary", "topics": [ "print.auditor_model_performance" ] }, { "page": "print.auditor_model_residual", "title": "Prints Model Residual Summary", "topics": [ "print.auditor_model_residual" ] }, { "page": "print.auditor_score", "title": "Prints of Models Scores", "topics": [ "print.auditor_score" ] }, { "page": "score", "title": "Model Scores computations", "topics": [ "score" ] }, { "page": "score_acc", "title": "Accuracy", "topics": [ "score_acc" ] }, { "page": "score_auc", "title": "Area Under ROC Curve (AUC)", "topics": [ "scoreROC", "score_auc" ] }, { "page": "score_auprc", "title": "Area under precision-recall curve", "topics": [ "score_auprc" ] }, { "page": "score_cooksdistance", "title": "Score based on Cooks Distance", "topics": [ "scoreCooksDistance", "score_cooksdistance" ] }, { "page": "score_dw", "title": "Durbin-Watson Score", "topics": [ "scoreDW", "score_dw" ] }, { "page": "score_f1", "title": "F1 Score", "topics": [ "score_f1" ] }, { "page": "score_gini", "title": "Gini Coefficient", "topics": [ "score_gini" ] }, { "page": "score_halfnormal", "title": "Half-Normal Score", "topics": [ "scoreHalfNormal", "score_halfnormal" ] }, { "page": "score_mae", "title": "Mean Absolute Error", "topics": [ "scoreMAE", "score_mae" ] }, { "page": "score_mse", "title": "Mean Square Error", "topics": [ "scoreMSE", "score_mse" ] }, { "page": "score_one_minus_acc", "title": "One minus accuracy", "topics": [ "score_one_minus_acc" ] }, { "page": "score_one_minus_auc", "title": "One minus Area Under ROC Curve (AUC)", "topics": [ "score_one_minus_auc" ] }, { "page": "score_one_minus_auprc", "title": "One Minus area under precision-recall curve", "topics": [ "score_one_minus_auprc" ] }, { "page": "score_one_minus_f1", "title": "One Minus F1 Score", "topics": [ "score_one_minus_f1" ] }, { "page": "score_one_minus_gini", "title": "One minus Gini Coefficient", "topics": [ "score_one_minus_gini" ] }, { "page": "score_one_minus_precision", "title": "One Minus Precision", "topics": [ "score_one_minus_precision" ] }, { "page": "score_one_minus_recall", "title": "One minus recall", "topics": [ "score_one_minus_recall" ] }, { "page": "score_one_minus_specificity", "title": "One minus specificity", "topics": [ "score_one_minus_specificity" ] }, { "page": "score_peak", "title": "Peak Score", "topics": [ "scorePeak", "score_peak" ] }, { "page": "score_precision", "title": "Precision", "topics": [ "score_precision" ] }, { "page": "score_r2", "title": "R-squared", "topics": [ "score_r2" ] }, { "page": "score_rec", "title": "Area Over the Curve for REC Curves", "topics": [ "scoreREC", "score_rec" ] }, { "page": "score_recall", "title": "Recall", "topics": [ "score_recall" ] }, { "page": "score_rmse", "title": "Root Mean Square Error", "topics": [ "scoreRMSE", "score_rmse" ] }, { "page": "score_rroc", "title": "Area Over the Curve for RROC Curves", "topics": [ "scoreRROC", "score_rroc" ] }, { "page": "score_runs", "title": "Runs Score", "topics": [ "scoreRuns", "score_runs" ] }, { "page": "score_specificity", "title": "Specificity", "topics": [ "score_specificity" ] } ], "_pkglogo": "https://github.com/modeloriented/auditor/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/modeloriented/auditor/raw/HEAD/README.md", "_rundeps": [ "cli", "codetools", "cpp11", "DALEX", "digest", "doFuture", "farver", "foreach", "future", "future.apply", "ggplot2", "ggrepel", "globals", "glue", "gridExtra", "gtable", "hnp", "iBreakDown", "ingredients", "isoband", "iterators", "kernelshap", "labeling", "lifecycle", "listenv", "MASS", "parallelly", "R6", "RColorBrewer", "Rcpp", "rlang", "S7", "scales", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": "model_evaluation_audit.Rmd", "filename": "model_evaluation_audit.html", "title": "Model evaluation audit", "author": "Alicja Gosiewska", "engine": "knitr::knitr", "headings": [ "Data", "Models", "Preparation for evaluation analysis", "Plots", "Receiver operating characteristic (ROC)", "LIFT chart", "Other methods" ], "created": "2018-07-23 14:43:25", "modified": "2021-07-19 12:43:20", "commits": 12 }, { "source": "model_fit_audit.Rmd", "filename": "model_fit_audit.html", "title": "Model fit audit", "author": "Alicja Gosiewska", "engine": "knitr::knitr", "headings": [ "Use-case - regression problem", "Half-normal plot", "Binomial model", "Use-cases (classification)", "Other methods", "References" ], "created": "2018-07-22 17:56:40", "modified": "2021-07-19 12:43:20", "commits": 12 }, { "source": "model_performance_audit.Rmd", "filename": "model_performance_audit.html", "title": "Model performance audit", "author": "Alicja Gosiewska", "engine": "knitr::knitr", "headings": [ "Data", "Models", "Preparation for analysis of performance", "Model ranking radar plot", "Other methods" ], "created": "2018-07-17 18:56:30", "modified": "2021-07-26 16:34:20", "commits": 12 }, { "source": "model_residuals_audit.Rmd", "filename": "model_residuals_audit.html", "title": "Model residuals audit", "author": "Alicja Gosiewska, Tomasz Mikołajczyk", "engine": "knitr::knitr", "headings": [ "Data", "Models", "Preparation for residual (error) analysis", "Plots", "Observed vs predicted", "Residuals vs observed, fitted or variable values", "Density of residuals", "Boxplot of residuals", "Autocorrelation function of residuals", "Autocorrelation of residuals", "Correlation of models", "Principal component analysis (PCA) of models", "Regression error characteristic curve (REC)", "Regression receiver operating characteristic (RROC)", "Scale location", "Two-sided empirical cumulative distribution function (TSECDF)", "Other methods" ], "created": "2018-07-22 17:56:40", "modified": "2021-07-26 16:34:20", "commits": 14 }, { "source": "observation_influence_audit.Rmd", "filename": "observation_influence_audit.html", "title": "Observation influence audit", "author": "Alicja Gosiewska", "engine": "knitr::knitr", "headings": [ "Data", "Models", "Preparation for analysis of observation influence", "Plot of Cook's distances", "Other methods" ], "created": "2018-07-22 17:56:40", "modified": "2021-07-26 16:34:20", "commits": 11 } ], "_score": 9.358949965938887, "_indexed": true, "_nocasepkg": "auditor", "_universes": [ "modeloriented", "agosiewska" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.3.5", "date": "2026-05-17T07:44:21.000Z", "distro": "noble", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "8c08d94b45b0361f2c79b1d15bf92fa0345cdaedbfbb9dd49238c3cd75066562", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.6.0", "os": "linux", "version": "1.3.5", "date": "2026-05-17T07:44:19.000Z", "distro": "noble", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "848f2ef0797d593c0bf8006c66d612c12d7d105f17f7edde37a3023ce3735873", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.5.3", "os": "mac", "version": "1.3.5", "date": "2026-05-17T07:43:52.000Z", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "95e4810ee042c2cbd97de950b150240a677c1eb383c78a37c721991a699605fd", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.6.0", "os": "mac", "version": "1.3.5", "date": "2026-05-17T07:43:33.000Z", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "0f490f8eec03b856366dd37ef74b1102bfc0ff20494ba112ebdd284b7c3fbb40", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.7.0", "os": "win", "version": "1.3.5", "date": "2026-05-17T07:43:40.000Z", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "4334051b551a9c985e05ab2d83f9e817c6bdc5966ea07769421288ec7fabf62f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.5.3", "os": "win", "version": "1.3.5", "date": "2026-05-17T07:43:30.000Z", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "efe20a3834cf8201a382a4d12433d89dfc8953581d46fc21c6d0deff4d6a7bc3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.6.0", "os": "win", "version": "1.3.5", "date": "2026-05-17T07:43:38.000Z", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "10791eca55576efd5820b8b63e0a130b01bb507bb284c6156a21802a508c07a0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" }, { "r": "4.6.0", "os": "wasm", "version": "1.3.5", "date": "2026-06-02T17:26:14.000Z", "commit": "1f2fdc125017e936b7936657e7fc4305bc2d4d06", "fileid": "08a572408a58f51adfc1cd10c9f0be71090aae35584cad207bb7bad7ea92a779", "status": "success", "buildurl": "https://github.com/r-universe/modeloriented/actions/runs/25984844296" } ] }