{ "_id": "6a27d81324555f66ed5466c2", "Package": "AutoMLR", "Type": "Package", "Title": "Automated Multi-Outcome Machine Learning Combination Models", "Version": "1.0.0", "Authors@R": "person(\"Peng\", \"Luo\", email = \"luopeng@smu.edu.cn\", role = c(\"aut\", \"cre\"))", "Description": "Provides automated machine learning workflows for survival\nanalysis, binary classification, continuous outcomes, and\nordinal outcomes. The package trains and combines model\nvariants across user-supplied multi-cohort data, evaluates\nsurvival models by leave-one-out cross-validation using\nHarrell's concordance index, binary models by leave-one-out\ncross-validation using receiver operating characteristic area\nunder the curve, continuous models by out-of-fold root mean\nsquared error and R-squared, and ordinal models by out-of-fold\nquadratic weighted kappa. It renders reproducible reports in\nHypertext Markup Language (HTML) with figures and diagnostics.\nThe survival workflow supports penalized and tree-based Cox\nproportional hazards models, stepwise Cox models, partial least\nsquares regression for Cox models, supervised principal\ncomponents, gradient boosting machine Cox models, survival\nsupport vector machines (survival-SVM), random survival\nforests, and optional 'CoxBoost'. The binary workflow supports\npenalized logistic regression, logistic baselines, gradient\nboosting machines, random forests, principal component analysis\n(PCA) logistic regression, and Gaussian naive Bayes variants.\nContinuous and ordinal workflows reuse an 18-variant regression\nregistry with penalized, linear, boosted, forest, PCA, and\nbaseline families. The optional 'CoxBoost' model is enabled\nwhen the suggested 'CoxBoost' package is installed; it is used\nconditionally and is not a strong dependency. Optional model\nbackends are checked at run time so missing backend packages\nskip only the affected model variants rather than blocking\ninstallation of the whole package. Methods build on Friedman et\nal. (2010) , Bair and Tibshirani\n(2004) , Ishwaran et al.\n(2008) , Blanche et al. (2013)\n, and Binder and Schumacher (2008)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Config/testthat/edition": "3", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-09 09:02:47 UTC", "User": "root" }, "Author": "Peng Luo [aut, cre]", "Maintainer": "Peng Luo ", "Repository": "https://robinllab.r-universe.dev", "Date/Publication": "2026-06-07 18:50:19 UTC", "RemoteUrl": "https://github.com/cran/AutoMLR", "RemoteRef": "HEAD", "RemoteSha": "3d225f8841dd5dce7969ac8f7cc64c95b5f847a0", "MD5sum": "37d6cdf0f9f4cfd418be25bcfe29862c", "_user": "robinllab", "_type": "src", "_file": "AutoMLR_1.0.0.tar.gz", "_fileid": "1d1503fda7481bef0b8483cab41a3d40297fdfe233caec7246eea46f6cbb9dbe", "_filesize": 4032075, "_sha256": "1d1503fda7481bef0b8483cab41a3d40297fdfe233caec7246eea46f6cbb9dbe", "_created": "2026-06-09T09:02:47.000Z", "_published": "2026-06-09T09:08:35.773Z", "_distro": "noble", "_jobs": [ { "job": 80286196638, "time": 248, "config": 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fitting.", "topics": [ "count_binary_combinations" ] }, { "page": "count_continuous_combinations", "title": "Count continuous model combinations without fitting.", "topics": [ "count_continuous_combinations" ] }, { "page": "count_ordinal_combinations", "title": "Count ordinal model combinations without fitting.", "topics": [ "count_ordinal_combinations" ] }, { "page": "count_surv_combinations", "title": "Count model combinations without fitting models.", "topics": [ "count_surv_combinations" ] }, { "page": "disable_auto_logging", "title": "Disable AutoMLR auto logging.", "topics": [ "disable_auto_logging" ] }, { "page": "evaluate_algorithm_loocv", "title": "Run LOOCV for a named algorithm in the registry.", "topics": [ "evaluate_algorithm_loocv" ] }, { "page": "evaluate_algorithms_loocv", "title": "Evaluate multiple survival model variants by LOOCV C-index.", "topics": [ "evaluate_algorithms_loocv" ] }, { "page": "evaluate_binary_algorithm_loocv", "title": "Evaluate one binary algorithm by LOOCV AUC.", "topics": [ "evaluate_binary_algorithm_loocv" ] }, { "page": "evaluate_binary_algorithms_loocv", "title": "Evaluate binary model variants by LOOCV AUC.", "topics": [ "evaluate_binary_algorithms_loocv" ] }, { "page": "evaluate_binary_combinations", "title": "Evaluate all-subset binary probability combinations.", "topics": [ "evaluate_binary_combinations" ] }, { "page": "evaluate_continuous_algorithm", "title": "Evaluate one continuous algorithm by out-of-fold performance.", "topics": [ "evaluate_continuous_algorithm" ] }, { "page": "evaluate_continuous_algorithms", "title": "Evaluate continuous model variants by out-of-fold performance.", "topics": [ "evaluate_continuous_algorithms" ] }, { "page": "evaluate_continuous_combinations", "title": "Evaluate all-subset continuous prediction combinations.", "topics": [ "evaluate_continuous_combinations" ] }, { "page": "evaluate_ordinal_algorithms", "title": "Evaluate ordinal model variants by out-of-fold performance.", "topics": [ "evaluate_ordinal_algorithms" ] }, { "page": "evaluate_ordinal_combinations", "title": "Evaluate all-subset ordinal score combinations.", "topics": [ "evaluate_ordinal_combinations" ] }, { "page": "evaluate_surv_combinations", "title": "Evaluate all-subset survival model combinations.", "topics": [ "evaluate_surv_combinations" ] }, { "page": "export_binary_results", "title": "Export binary AutoMLR results.", "topics": [ "export_binary_results" ] }, { "page": "export_continuous_results", "title": "Export continuous AutoMLR results.", "topics": [ "export_continuous_results" ] }, { "page": "export_extreme_screen_results", "title": "Export extreme-screening tables and publication-style audit figures", "topics": [ "export_extreme_screen_results" ] }, { "page": "export_ordinal_results", "title": "Export ordinal AutoMLR results.", "topics": [ "export_ordinal_results" ] }, { "page": "export_surv_results", "title": "Export AutoMLR survival results as a reproducible result bundle.", "topics": [ "export_surv_results" ] }, { "page": "extreme_surv_screen", "title": "Extreme two-stage screening for survival model combinations", "topics": [ "extreme_surv_screen" ] }, { "page": "fit_binary_ensemble", "title": "Fit a binary probability ensemble.", "topics": [ "fit_binary_ensemble" ] }, { "page": "fit_continuous_ensemble", "title": "Fit a continuous-outcome prediction ensemble.", "topics": [ "fit_continuous_ensemble" ] }, { "page": "fit_ordinal_ensemble", "title": "Fit an ordinal-outcome ensemble.", "topics": [ "fit_ordinal_ensemble" ] }, { "page": "fit_surv_ensemble", "title": "Fit a weighted ensemble of survival-risk models.", "topics": [ "fit_surv_ensemble" ] }, { "page": "get_binary_registry", "title": "Return the binary-classification algorithm registry.", "topics": [ "get_binary_registry" ] }, { "page": "get_continuous_registry", "title": "Return the continuous-outcome algorithm registry.", "topics": [ "get_continuous_registry" ] }, { "page": "get_ordinal_registry", "title": "Return the ordinal-outcome algorithm registry.", "topics": [ "get_ordinal_registry" ] }, { "page": "get_surv_registry", "title": "Return the full survival-algorithm registry.", "topics": [ "get_surv_registry" ] }, { "page": "initialize_auto_logging", "title": "Enable file + console logging for the current R session.", "topics": [ "initialize_auto_logging" ] }, { "page": "list_binary_algorithms", "title": "List supported binary-classification algorithms.", "topics": [ "list_binary_algorithms" ] }, { "page": "list_binary_model_variants", "title": "List binary-classification model variants.", "topics": [ "list_binary_model_variants" ] }, { "page": "list_continuous_algorithms", "title": "List supported continuous-outcome algorithms.", "topics": [ "list_continuous_algorithms" ] }, { "page": "list_continuous_model_variants", "title": "List continuous-outcome model variants.", "topics": [ "list_continuous_model_variants" ] }, { "page": "list_model_variants", "title": "List concrete model variants generated from algorithm grids.", "topics": [ "list_model_variants" ] }, { "page": "list_ordinal_algorithms", "title": "List supported ordinal-outcome algorithms.", "topics": [ "list_ordinal_algorithms" ] }, { "page": "list_ordinal_model_variants", "title": "List ordinal-outcome model variants.", "topics": [ "list_ordinal_model_variants" ] }, { "page": "list_surv_algorithms", "title": "List the supported survival algorithms (keys).", "topics": [ "list_surv_algorithms" ] }, { "page": "loocv_auc", "title": "Leave-one-out cross-validation AUC for one binary algorithm.", "topics": [ "loocv_auc" ] }, { "page": "loocv_cindex", "title": "Leave-one-out cross-validation C-index for one survival algorithm.", "topics": [ "loocv_cindex" ] }, { "page": "ordinal_accuracy", "title": "Accuracy for ordinal class predictions.", "topics": [ "ordinal_accuracy" ] }, { "page": "ordinal_balanced_accuracy", "title": "Balanced accuracy for ordinal class predictions.", "topics": [ "ordinal_balanced_accuracy" ] }, { "page": "ordinal_mae", "title": "Mean absolute class error for ordinal predictions.", "topics": [ "ordinal_mae" ] }, { "page": "ordinal_qwk", "title": "Quadratic weighted kappa for ordinal predictions.", "topics": [ "ordinal_qwk" ] }, { "page": "ordinalmlr_parameters", "title": "Default parameters for AutoMLR ordinal-outcome workflows.", "topics": [ "ordinalmlr_parameters" ] }, { "page": "parallel_lapply", "title": "Parallel 'lapply' that transparently falls back to sequential.", "topics": [ "parallel_lapply" ] }, { "page": "predict.automlr_binary_ensemble", "title": "Predict binary ensemble probabilities or classes.", "topics": [ "predict.automlr_binary_ensemble" ] }, { "page": "predict.automlr_continuous_ensemble", "title": "Predict continuous ensemble values.", "topics": [ "predict.automlr_continuous_ensemble" ] }, { "page": "predict.automlr_ordinal_ensemble", "title": "Predict ordinal ensemble scores or classes.", "topics": [ 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survival screening", "topics": [ "print.automlr_extreme_screen" ] }, { "page": "recommend_binary_auc_threshold", "title": "Recommend a binary AUC cutoff from candidate model results.", "topics": [ "recommend_binary_auc_threshold" ] }, { "page": "recommend_continuous_r2_threshold", "title": "Recommend a continuous R-squared cutoff from candidate model results.", "topics": [ "recommend_continuous_r2_threshold" ] }, { "page": "recommend_ordinal_qwk_threshold", "title": "Recommend an ordinal kappa cutoff from candidate model results.", "topics": [ "recommend_ordinal_qwk_threshold" ] }, { "page": "recommend_surv_cindex_threshold", "title": "Recommend a survival C-index cutoff from candidate model results.", "topics": [ "recommend_surv_cindex_threshold" ] }, { "page": "render_binary_report", "title": "Render an HTML report for a fitted binary ensemble.", "topics": [ "render_binary_report" ] }, { "page": "render_continuous_report", "title": "Render an HTML report for a fitted continuous ensemble.", "topics": [ "render_continuous_report" ] }, { "page": "render_ordinal_report", "title": "Render an HTML report for a fitted ordinal ensemble.", "topics": [ "render_ordinal_report" ] }, { "page": "render_surv_report", "title": "Render an HTML report for a fitted survival ensemble.", "topics": [ "render_surv_report" ] }, { "page": "report_binary_cohort_intersection", "title": "Print a binary cohort-intersection report.", "topics": [ "report_binary_cohort_intersection" ] }, { "page": "report_cohort_intersection", "title": "Print a human-readable report of the cohort intersection.", "topics": [ "report_cohort_intersection" ] }, { "page": "report_continuous_cohort_intersection", "title": "Print a continuous cohort-intersection report.", "topics": [ "report_continuous_cohort_intersection" ] }, { "page": "report_ordinal_cohort_intersection", "title": "Print an ordinal cohort-intersection report.", "topics": [ "report_ordinal_cohort_intersection" ] }, { "page": "start_parallel", 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