{ "_id": "6a1f1231b401979e7341dcaa", "Package": "randomUniformForest", "Type": "Package", "Title": "Random Uniform Forests for Classification, Regression and\nUnsupervised Learning", "Version": "1.1.6", "Date": "2022-05-31", "Author": "Saip Ciss", "Maintainer": "Saip Ciss ", "Description": "Ensemble model, for classification, regression and\nunsupervised learning, based on a forest of unpruned and\nrandomized binary decision trees. Each tree is grown by\nsampling, with replacement, a set of variables at each node.\nEach cut-point is generated randomly, according to the\ncontinuous Uniform distribution. For each tree, data are either\nbootstrapped or subsampled. The unsupervised mode introduces\nclustering, dimension reduction and variable importance, using\na three-layer engine. Random Uniform Forests are mainly aimed\nto lower correlation between trees (or trees residuals), to\nprovide a deep analysis of variable importance and to allow\nnative distributed and incremental learning.", "License": "BSD_3_clause + file LICENSE", "LazyData": "yes", "VignetteBuilder": "R.rsp", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-17 07:43:05 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2022-06-21 20:50:02 UTC", "RemoteUrl": "https://github.com/cran/randomUniformForest", "RemoteRef": "HEAD", "RemoteSha": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "MD5sum": "1d8fa137b17e5a11663dd2bd0ca29ac4", "_user": "cran", "_type": "src", "_file": "randomUniformForest_1.1.6.tar.gz", "_fileid": "4a19b34c0285f43510ef56e3ae0a2e6ef9235599125078a3b1a15f90fbaa7728", "_filesize": 1598597, "_sha256": "4a19b34c0285f43510ef56e3ae0a2e6ef9235599125078a3b1a15f90fbaa7728", "_created": "2026-05-17T07:43:05.000Z", "_published": "2026-06-02T17:26:09.443Z", "_distro": "noble", "_jobs": [ { "job": 79131494488, "time": 148, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7040536984" }, { "job": 79131494204, "time": 152, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7040537490" }, { "job": 79131494887, "time": 157, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7040537969" }, { "job": 79131494457, "time": 165, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7040538769" }, { "job": 79131493769, "time": 227, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7040520504" }, { "job": 79131493665, "time": 112, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7365050910" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/25984872806", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/randomUniformForest", "_commit": { "id": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "author": "Saip Ciss ", "committer": "cran-robot ", "message": "version 1.1.6\n", "time": 1655844602 }, "_maintainer": { "name": "Saip Ciss", "email": "saip.ciss@wanadoo.fr" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.2.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "methods", "role": "Imports" }, { "package": "Rcpp", "version": ">= 0.11.1", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "doParallel", "role": "Imports" }, { "package": "iterators", "role": "Imports" }, { "package": "foreach", "version": ">= 1.4.2", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "pROC", "role": "Imports" }, { "package": "cluster", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "R.rsp", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 4, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 216, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/randomUniformForest" }, "_searchresults": 93, "_topics": [ "cpp" ], "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/randomUniformForest.html", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, "_releases": [ { "version": "1.0.6", "date": "2014-05-29" }, { "version": "1.0.8", "date": "2014-09-19" }, { "version": "1.0.9", "date": "2014-11-07" }, { "version": "1.1.0", "date": "2014-11-10" }, { "version": "1.1.1", "date": "2014-12-03" }, { "version": "1.1.2", "date": "2015-01-05" }, { "version": "1.1.5", "date": "2015-02-16" }, { "version": "1.1.6", "date": "2022-06-22" } ], "_exports": [ "as.supervised", "as.true.matrix", "asymetricCrossEntropyCPP", "asymetricGiniCPP", "asymetricInformationGainCPP", "bCI", "bCICore", "biasVarCov", "CheckSameValuesInAllAttributes", "CheckSameValuesInLabels", "checkUniqueObsCPP", "classifyCPP", "classifyMatrixCPP", "clusterAnalysis", "clusteringObservations", "combineRUFObjects", "combineUnsupervised", "concat", "concatCore", "conditionalCrossEntropyCPP", "conditionalGiniCPP", "confusion.matrix", "copulaLike", "count.factor", "crossEntropyCPP", "dates2numeric", "define_train_test_sets", "difflog", "dummy.recode", "entropyInformationGainCPP", "estimatePredictionAccuracy", "estimaterequiredSampleSize", "expectedSquaredBias", "extractYFromData", "factor2matrix", "factor2vector", "fillNA2.randomUniformForest", "fillVariablesNames", "fillWith", "filter.forest", "filter.object", "filterOutliers", "find.first.idx", "find.idx", "find.root", "fScore", "fullNode", "gap.stats", "generalization.error", "generic.cv", "generic.log", "generic.smoothing.log", "genericCbind", "genericNode", "genericOutput", "getCorr", "getOddEven", "getTree.randomUniformForest", "getVotesProbability", "getVotesProbability2", "giniCPP", "gMean", "hClust", "HuberDist", "Id", "importance", "importance.randomUniformForest", "inDummies", "init_values", "insert.in.vector", "insert.in.vector2", "interClassesVariance", "intraClassesVariance", "is.wholenumber", "kappaStat", "kBiggestProximities", "keep.index", "kMeans", "L1AsymetricInformationGainCPP", "L1Dist", "L1DistCPP", "L1InformationGainCPP", "L2.logDist", "L2AsymetricInformationGainCPP", "L2Dist", "L2DistCPP", "L2InformationGainCPP", "lagFunction", "leafNode", "LInfCPP", "localTreeImportance", "localVariableImportance", "majorityClass", "matrix2factor", "matrix2factor2", "MDSscale", "mergeClusters", "mergeLists", "mergeOutliers", "min_or_max", "model.stats", "modelingResiduals", "modifyClusters", "modX", "monitorOOBError", "myAUC", "na.impute", "na.missing", "na.replace", "NAfactor2matrix", "NAFeatures", "NATreatment", "observationsImportance", "onlineClassify", "onlineCombineRUF", "OOBquantiles", "OOBVotesScale", "optimizeFalsePositives", "options.filter", "outputPerturbationSampling", "outsideConfIntLevels", "overSampling", "parallelNA.replace", "partialDependenceBetweenPredictors", "partialDependenceOverResponses", "partialImportance", "permuteCatValues", "perspWithcol", "plot.importance", "plot.randomUniformForest", "plot.unsupervised", "plotTree", "plotTreeCore", "plotTreeCore2", "postProcessingVotes", "predict.randomUniformForest", "predictDecisionTree", "predictionvsResponses", "print.importance", "print.randomUniformForest", "print.unsupervised", "proximitiesMatrix", "pseudoHuberDist", "pseudoNAReplace", "randomCombination", "randomize", "randomUniformForest", "randomUniformForest.default", "randomUniformForest.formula", "randomUniformForestCore", "randomUniformForestCore.big", "randomUniformForestCore.merge", "randomUniformForestCore.predict", "randomWhichMax", "rankingTrainData", "reduce.trees", "residualsRandomUniformForest", "reSMOTE", "rewind.trees", "rm.coordinates", "rm.correlation", "rm.InAList", "rm.string", "rm.tempdir", "rm.trees", "rmInAListByNames", "rmInf", "rmNA", "rmNoise", "roc.curve", "rollApplyFunction", "rufImpute", "runifMatrixCPP", "rUniformForest.big", "rUniformForest.combine", "rUniformForest.grow", "rUniformForest.merge", "rUniformForestPredict", "scale2AnyValues", "scalingMDS", "setManyDatasets", "simulationData", "smoothing.log", "someErrorType", "sortCPP", "sortDataframe", "sortMatrix", "specClust", "splitClusters", "splitVarCore", "standardize", "standardize_vect", "strength_and_correlation", "subsampleFile", "summary.randomUniformForest", "timer", "timeStampCore", "twoColumnsImportance", "uniformDecisionTree", "unsupervised", "unsupervised.randomUniformForest", "unsupervised2supervised", "update.unsupervised", "updateCombined.unsupervised", "variance", "vector2factor", "vector2matrix", "weightedVote", "weightedVoteModel", "which.is.duplicate", "which.is.factor", "which.is.na", "which.is.nearestCenter", "which.is.wholenumber", "XMinMaxCPP" ], "_datasets": [ { "name": "autoMPG", "title": "Auto MPG Data Set", "object": "autoMPG", "class": [ "data.frame" ], "fields": [ "mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model year", "origin", "car name" ], "rows": 398, "table": true, "tojson": true }, { "name": "breastCancer", "title": "Breast Cancer Wisconsin (Original) Data Set", "object": "breastCancer", "class": [ "data.frame" ], "fields": [ "Sample code number", "Clump Thickness", "Uniformity of Cell Size", "Uniformity of Cell Shape", "Marginal Adhesion", "Single Epithelial Cell Size", "Bare Nuclei", "Bland Chromatin", "Normal Nucleoli", " Mitoses", "Class" ], "rows": 699, "table": true, "tojson": true }, { "name": "carEvaluation", "title": "Car Evaluation Data Set", "object": "carEvaluation", "class": [ "data.frame" ], "fields": [ "buying", "priceOfMaintenance", "nbDoors", "nbPersons", "luggageBoot", "safety", "class" ], "rows": 1728, "table": true, "tojson": true }, { "name": "ConcreteCompressiveStrength", "title": "Concrete Compressive Strength Data Set", "object": "ConcreteCompressiveStrength", "class": [ "data.frame" ], "fields": [ "Cement", "Blast Furnace Slag", "Fly Ash", "Water", "Superplasticizer", "Coarse Aggregate", "Fine Aggregate", "Age", "Concrete compressive strength" ], "rows": 1030, "table": true, "tojson": true }, { "name": "wineQualityRed", "title": "Wine Quality Data Set", "object": "wineQualityRed", "class": [ "data.frame" ], "fields": [ "fixed.acidity", "volatile.acidity", "citric.acid", "residual.sugar", "chlorides", "free.sulfur.dioxide", "total.sulfur.dioxide", "density", "pH", "sulphates", "alcohol", "quality" ], "rows": 1599, "table": true, "tojson": true } ], "_help": [ { "page": "randomUniformForest-package", "title": "Random Uniform Forests for Classification, Regression and Unsupervised Learning", "concept": [ "ensemble methods", "supervised learning", "unsupervised learning", "classification", "regression", "Random Forests", "Random Uniform Forests", "clustering", "dimension reduction", "variable importance" ], "topics": [ "randomUniformForest-package" ] }, { "page": "as.supervised", "title": "Conversion of an unsupervised model into a supervised one", "topics": [ "as.supervised" ] }, { "page": "autoMPG", "title": "Auto MPG Data Set", "topics": [ "autoMPG" ] }, { "page": "bCI", "title": "Bootstrapped Prediction Intervals for Ensemble Models", "concept": [ "confidence interval", "prediction intervals" ], "topics": [ "bCI" ] }, { "page": "biasVarCov", "title": "Bias-Variance-Covariance Decomposition", "concept": [ "MSE decomposition", "generalization error decomposition" ], "topics": [ "biasVarCov" ] }, { "page": "breastCancer", "title": "Breast Cancer Wisconsin (Original) Data Set", "topics": [ "breastCancer" ] }, { "page": "carEvaluation", "title": "Car Evaluation Data Set", "topics": [ "carEvaluation" ] }, { "page": "clusterAnalysis", "title": "Cluster (or classes) analysis of importance objects.", "concept": [ "Variable Importance analysis" ], "topics": [ "clusterAnalysis" ] }, { "page": "clusteringObservations", "title": "Cluster observations of a (supervised) randomUniformForest object", "topics": [ "clusteringObservations" ] }, { "page": "combineUnsupervised", "title": "Combine Unsupervised Learning objects", "topics": [ "combineUnsupervised" ] }, { "page": "ConcreteCompressiveStrength", "title": "Concrete Compressive Strength Data Set", "topics": [ "ConcreteCompressiveStrength" ] }, { "page": "fillNA2.randomUniformForest", "title": "Missing values imputation by randomUniformForest", "topics": [ "fillNA2.randomUniformForest", "rufImpute" ] }, { "page": "generic.cv", "title": "Generic k-fold cross-validation", "topics": [ "generic.cv" ] }, { "page": "getTree.randomUniformForest", "title": "Extract a tree from a forest", "topics": [ "getTree", "getTree.randomUniformForest" ] }, { "page": "importance.randomUniformForest", "title": "Variable Importance for random Uniform Forests", "concept": [ "variable importance", "variable selection" ], "topics": [ "importance", "importance.randomUniformForest", "plot.importance", "print.importance" ] }, { "page": "init_values", "title": "Training and validation samples from data", "topics": [ "init_values" ] }, { "page": "internalFunctions", "title": "All internal functions", "topics": [ "A2Rplot", "A2Rplot.default", "A2Rplot.hclust", "as.true.matrix", "asymetricCrossEntropyCPP", "asymetricGiniCPP", "asymetricInformationGainCPP", "bCICore", "category2Proba", "category2Quantile", "categoryCombination", "CheckSameValuesInAllAttributes", "CheckSameValuesInLabels", "checkUniqueObsCPP", "classifyCPP", "classifyMatrixCPP", "combineRUFObjects", "concat", "concatCore", "conditionalCrossEntropyCPP", "conditionalGiniCPP", "confusion.matrix", "copulaLike", "count.factor", "crossEntropyCPP", "cutree.order", "dates2numeric", "define_train_test_sets", "difflog", "dummy.recode", "entropyInformationGainCPP", "estimatePredictionAccuracy", "estimaterequiredSampleSize", "expectedSquaredBias", "extractYFromData", "factor2matrix", "factor2vector", "fillVariablesNames", "fillWith", "filter.forest", "filter.object", "filterOutliers", "find.first.idx", "find.idx", "find.root", "fScore", "fullNDCG", "fullNode", "gap.stats", "generalization.error", "generic.log", "generic.smoothing.log", "genericCbind", "genericNode", "genericOutput", "getCorr", "getOddEven", "getVotesProbability", "getVotesProbability2", "giniCPP", "gMean", "hClust", "HuberDist", "Id", "imputeCategoryForTestData", "inDummies", "insert.in.vector", "insert.in.vector2", "interClassesVariance", "intraClassesVariance", "is.wholenumber", "kappaStat", "kBiggestProximities", "keep.index", "kMeans", "L1AsymetricInformationGainCPP", "L1Dist", "L1DistCPP", "L1InformationGainCPP", "L2.logDist", "L2AsymetricInformationGainCPP", "L2Dist", "L2DistCPP", "L2InformationGainCPP", "lagFunction", "leafNode", "LInfCPP", "localTreeImportance", "localVariableImportance", "majorityClass", "matrix2factor", "matrix2factor2", "MDSscale", "mergeLists", "mergeOutliers", "min_or_max", "modelingResiduals", "modX", "monitorOOBError", "myAUC", "na.impute", "na.missing", "na.replace", "NAfactor2matrix", "NAFeatures", "NATreatment", "ndcg", "observationsImportance", "onlineClassify", "onlineCombineRUF", "OOBquantiles", "OOBVotesScale", "optimizeFalsePositives", "options.filter", "outputPerturbationSampling", "outsideConfIntLevels", "overSampling", "parallelNA.replace", "permuteCatValues", "perspWithcol", "plotTreeCore", "plotTreeCore2", "predictDecisionTree", "predictionvsResponses", "proximitiesMatrix", "pseudoHuberDist", "pseudoNAReplace", "randomCombination", "randomize", "randomUniformForestCore", "randomUniformForestCore.big", "randomUniformForestCore.merge", "randomUniformForestCore.predict", "randomWhichMax", "rankingTrainData", "reduce.trees", "residualsRandomUniformForest", "rewind.trees", "rm.coordinates", "rm.correlation", "rm.InAList", "rm.string", "rm.tempdir", "rmInAListByNames", "rmInf", "rmNA", "rmNoise", "rollApplyFunction", "runifMatrixCPP", "rUniformForest.merge", "rUniformForestPredict", "sampleDirichlet", "scale2AnyValues", "scalingMDS", "setManyDatasets", "smoothing.log", "someErrorType", "sortCPP", "sortDataframe", "sortMatrix", "specClust", "splitVarCore", "standardize", "standardize_vect", "strength_and_correlation", "subsampleFile", "timer", "timeStampCore", "twoColumnsImportance", "uniformDecisionTree", "unsupervised2supervised", "updateCombined.unsupervised", "variance", "vector2factor", "vector2matrix", "weightedVote", "weightedVoteModel", "which.is.duplicate", "which.is.factor", "which.is.na", "which.is.nearestCenter", "which.is.wholenumber", "XMinMaxCPP" ] }, { "page": "mergeClusters", "title": "Merge two arbitrary, but adjacent, clusters", "topics": [ "mergeClusters" ] }, { "page": "model.stats", "title": "Common statistics for a vector (or factor) of predictions and a vector (or factor) of responses", "topics": [ "model.stats" ] }, { "page": "modifyClusters", "title": "Change number of clusters (and clusters shape) on the fly", "topics": [ "modifyClusters" ] }, { "page": "partialDependenceBetweenPredictors", "title": "Partial Dependence between Predictors and effect over Response", "concept": [ "partial dependence" ], "topics": [ "partialDependenceBetweenPredictors" ] }, { "page": "partialDependenceOverResponses", "title": "Partial Dependence Plots and Models", "concept": [ "partial dependence" ], "topics": [ "partialDependenceOverResponses" ] }, { "page": "partialImportance", "title": "Partial Importance for random Uniform Forests", "topics": [ "partialImportance" ] }, { "page": "plotTree", "title": "Plot a Random Uniform Decision Tree", "topics": [ "plotTree" ] }, { "page": "postProcessingVotes", "title": "Post-processing for Regression", "topics": [ "postProcessingVotes" ] }, { "page": "predict.randomUniformForest", "title": "Predict method for random Uniform Forests objects", "topics": [ "predict", "predict.randomUniformForest" ] }, { "page": "randomUniformForest", "title": "Random Uniform Forests for Classification, Regression and Unsupervised Learning", "concept": [ "machine learning", "supervised learning", "classification", "regression", "decision tree", "ensemble methods" ], "topics": [ "plot.randomUniformForest", "print.randomUniformForest", "randomUniformForest", "randomUniformForest.default", "randomUniformForest.formula", "summary.randomUniformForest" ] }, { "page": "reSMOTE", "title": "REplication of a Synthetic Minority Oversampling TEchnique for highly imbalanced datasets", "topics": [ "reSMOTE" ] }, { "page": "rm.trees", "title": "Remove trees from a random Uniform Forest", "topics": [ "rm.trees" ] }, { "page": "roc.curve", "title": "ROC and precision-recall curves for random Uniform Forests", "topics": [ "roc.curve" ] }, { "page": "rUniformForest.big", "title": "Random Uniform Forests for Classification and Regression with large data sets", "topics": [ "rUniformForest.big" ] }, { "page": "rUniformForest.combine", "title": "Incremental learning for random Uniform Forests", "topics": [ "rUniformForest.combine" ] }, { "page": "rUniformForest.grow", "title": "Add trees to a random Uniform Forest", "topics": [ "rUniformForest.grow" ] }, { "page": "simulationData", "title": "Simulation of Gaussian vector", "topics": [ "simulationData" ] }, { "page": "splitClusters", "title": "Split a cluster on the fly", "topics": [ "splitClusters" ] }, { "page": "unsupervised.randomUniformForest", "title": "Unsupervised Learning with Random Uniform Forests", "concept": [ "unsupervised learning", "clustering", "dimension reduction", "variable importance" ], "topics": [ "plot.unsupervised", "print.unsupervised", "unsupervised", "unsupervised.randomUniformForest" ] }, { "page": "update.unsupervised", "title": "Update Unsupervised Learning object", "topics": [ "update", "update.unsupervised" ] }, { "page": "wineQualityRed", "title": "Wine Quality Data Set", "topics": [ "wineQualityRed" ] } ], "_rundeps": [ "cli", "cluster", "codetools", "cpp11", "doParallel", "farver", "foreach", "ggplot2", "glue", "gtable", "isoband", "iterators", "labeling", "lifecycle", "MASS", "pROC", "R6", "RColorBrewer", "Rcpp", "rlang", "S7", "scales", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "randomUniformForestsOverview.pdf.asis", "filename": "randomUniformForestsOverview.pdf", "title": "Random Uniform Forests in theory and practice", "engine": "R.rsp::asis", "headings": [], "created": "2014-09-18", "modified": "2014-09-18", "commits": 1 }, { "source": "VariableImportanceInRandomUniformForests.pdf.asis", "filename": "VariableImportanceInRandomUniformForests.pdf", "title": "Variable Importance in Random Uniform Forests", "engine": "R.rsp::asis", "headings": [], "created": "2015-12-05", "modified": "2015-12-05", "commits": 1 } ], "_score": 3.871572935545879, "_indexed": true, "_nocasepkg": "randomuniformforest", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.1.6", "date": "2026-05-17T07:45:13.000Z", "distro": "noble", "arch": "aarch64", "commit": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "fileid": "5f3e3e61eab6a401e9c1953cb98d4754eb61ad1479a26f9742f22fe7a3c989ee", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984872806" }, { "r": "4.7.0", "os": "linux", "version": "1.1.6", "date": "2026-05-17T07:45:13.000Z", "distro": "noble", "arch": "x86_64", "commit": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "fileid": "2020013b18dcbace15ce564987c15f868e4cde817eac655d7794ff527aa04590", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984872806" }, { "r": "4.6.0", "os": "linux", "version": "1.1.6", "date": "2026-05-17T07:45:18.000Z", "distro": "noble", "arch": "aarch64", "commit": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "fileid": "4823256659b539c283683d05b21676514155cd47d785ee962c345e9d1c0049af", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984872806" }, { "r": "4.6.0", "os": "linux", "version": "1.1.6", "date": "2026-05-17T07:45:20.000Z", "distro": "noble", "arch": "x86_64", "commit": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "fileid": "e231e15ca232ac8c3fbaef4c5596373c271252f002013aa38e36d8407bf089a2", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984872806" }, { "r": "4.6.0", "os": "wasm", "version": "1.1.6", "date": "2026-06-02T17:25:46.000Z", "arch": "emscripten", "commit": "f0176efdd3c11a349a73d4cf358e948ea7f1211e", "fileid": "40bf981cbada2af57ef4e5f83be68e239f9b2e1ebc89fa3a34a2d7b4c7ce79ee", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/25984872806" } ] }