{ "_id": "6ac4aa17f511ae58c099fb1e", "Package": "MachineShop", "Type": "Package", "Title": "Machine Learning Models and Tools", "Version": "3.9.2", "Date": "2026-01-30", "Authors@R": "person(c(\"Brian\", \"J\"), \"Smith\",\nemail = \"brian-j-smith@uiowa.edu\",\nrole = c(\"aut\", \"cre\"))", "Author": "Brian J Smith [aut, cre]", "Maintainer": "Brian J Smith ", "Description": "Meta-package for statistical and machine learning with a\nunified interface for model fitting, prediction, performance\nassessment, and presentation of results. Approaches for model\nfitting and prediction of numerical, categorical, or censored\ntime-to-event outcomes include traditional regression models,\nregularization methods, tree-based methods, support vector\nmachines, neural networks, ensembles, data preprocessing,\nfiltering, and model tuning and selection. Performance metrics\nare provided for model assessment and can be estimated with\nindependent test sets, split sampling, cross-validation, or\nbootstrap resampling. Resample estimation can be executed in\nparallel for faster processing and nested in cases of model\ntuning and selection. Modeling results can be summarized with\ndescriptive statistics; calibration curves; variable\nimportance; partial dependence plots; confusion matrices; and\nROC, lift, and other performance curves.", "Additional_repositories": "https://brian-j-smith.github.io/drat", "LazyData": "true", "License": "GPL-3", "URL": "https://brian-j-smith.github.io/MachineShop/", "BugReports": "https://github.com/brian-j-smith/MachineShop/issues", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "Collate": "'classes.R' 'conditions.R' 'MachineShop-package.R'\n'MLControl.R' 'MLInput.R' 'MLMetric.R' 'MLModel.R'\n'MLOptimization.R' 'ML_AdaBagModel.R' 'ML_AdaBoostModel.R'\n'ML_BARTMachineModel.R' 'ML_BARTModel.R' 'ML_BlackBoostModel.R'\n'ML_C50Model.R' 'ML_CForestModel.R' 'ML_CoxModel.R'\n'ML_EarthModel.R' 'ML_FDAModel.R' 'ML_GAMBoostModel.R'\n'ML_GBMModel.R' 'ML_GLMBoostModel.R' 'ML_GLMModel.R'\n'ML_GLMNetModel.R' 'ML_KNNModel.R' 'ML_LARSModel.R'\n'ML_LDAModel.R' 'ML_LMModel.R' 'ML_MDAModel.R' 'ML_NNetModel.R'\n'ML_NaiveBayesModel.R' 'ML_ParsnipModel.R' 'ML_PLSModel.R'\n'ML_POLRModel.R' 'ML_QDAModel.R' 'ML_RFSRCModel.R'\n'ML_RPartModel.R' 'ML_RandomForestModel.R' 'ML_RangerModel.R'\n'ML_SVMModel.R' 'ML_StackedModel.R' 'ML_SuperModel.R'\n'ML_SurvRegModel.R' 'ML_TreeModel.R' 'ML_XGBModel.R'\n'ModelFrame.R' 'ModelRecipe.R' 'ModelSpecification.R'\n'TrainedInputs.R' 'TrainedModels.R' 'TrainingParams.R'\n'append.R' 'calibration.R' 'case_comps.R' 'coerce.R'\n'combine.R' 'confusion.R' 'convert.R' 'data.R' 'dependence.R'\n'diff.R' 'expand.R' 'extract.R' 'fit.R' 'grid.R' 'metricinfo.R'\n'metrics.R' 'metrics_factor.R' 'metrics_numeric.R'\n'modelinfo.R' 'models.R' 'performance.R' 'performance_curve.R'\n'plot.R' 'predict.R' 'print.R' 'recipe_roles.R' 'reexports.R'\n'resample.R' 'response.R' 'rfe.R' 'settings.R' 'step_kmeans.R'\n'step_kmedoids.R' 'step_lincomp.R' 'step_sbf.R' 'step_spca.R'\n'summary.R' 'survival.R' 'utils.R' 'varimp.R'", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://brian-j-smith.r-universe.dev", "Date/Publication": "2026-04-08 21:07:38 UTC", "RemoteUrl": "https://github.com/brian-j-smith/machineshop", "RemoteRef": "HEAD", "RemoteSha": "92489c0bbf1e1609897f1d479c6f7d2c1d83577f", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-05 09:17:55 UTC", "User": "root" }, "_user": "brian-j-smith", "_type": "src", "_file": "MachineShop_3.9.2.tar.gz", "_fileid": "https://r2.ropensci.org/5d50199fd35bafe3e1c4713faffe134fe6b159beb72e92b428fe213bda563fa1", "_filesize": 2159433, "_sha256": "5d50199fd35bafe3e1c4713faffe134fe6b159beb72e92b428fe213bda563fa1", "_expires": "2027-01-13T09:28:18.000Z", "_created": "2026-10-05T09:17:55.000Z", "_published": "2026-10-06T07:58:15.026Z", "_jobs": [ { "job": 112165205613, "time": 269, "config": "linux-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "11397511872" }, { "job": 112165205681, "time": 245, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": 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