{ "_id": "6a281bf7a476d7de6d3c6402", "Package": "pre", "Title": "Prediction Rule Ensembles", "Version": "1.0.9", "Authors@R": "c(person(\"Marjolein\", \"Fokkema\", role = c(\"aut\", \"cre\"), email = \n\"m.fokkema@fsw.leidenuniv.nl\"), person(given = \"Benjamin\",\nfamily = \"Christoffersen\", role = \"aut\", email = \"boennecd@gmail.com\"))", "Author": "Marjolein Fokkema [aut, cre], Benjamin Christoffersen [aut]", "Maintainer": "Marjolein Fokkema ", "Description": "Derives prediction rule ensembles (PREs). Largely follows\nthe procedure for deriving PREs as described in Friedman &\nPopescu (2008; ), with adjustments and\nimprovements described in Fokkema (2020;\n) and Fokkema & Strobl (2020;\n). The main function pre() derives\nprediction rule ensembles consisting of rules and/or linear\nterms for continuous, binary, count, multinomial, survival and\nmultivariate continuous responses. Function gpe() derives\ngeneralized prediction ensembles, consisting of rules, hinge\nand linear functions of the predictor variables.", "URL": "https://github.com/marjoleinF/pre", "BugReports": "https://github.com/marjoleinF/pre/issues", "License": "GPL-2 | GPL-3", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-09 13:54:12 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-09 09:40:02 UTC", "RemoteUrl": "https://github.com/cran/pre", "RemoteRef": "HEAD", "RemoteSha": "74a5498c555bc6507bc9b1731b0df1d6608edb49", "MD5sum": "e34718f7be31bbe701565aabf9834d07", "_user": "cran", "_type": "src", "_file": "pre_1.0.9.tar.gz", "_fileid": "3f3373542f9e9085c2ec0cfc702b2846f472df012fcdb6fa54d4e3daea0ef81d", "_filesize": 2445562, "_sha256": "3f3373542f9e9085c2ec0cfc702b2846f472df012fcdb6fa54d4e3daea0ef81d", "_created": "2026-06-09T13:54:12.000Z", "_published": "2026-06-09T13:58:15.121Z", "_distro": "noble", "_jobs": [ { "job": 80340077584, "time": 199, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7509881386" }, { "job": 80340077585, "time": 198, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7509880904" }, { "job": 80338980617, "time": 292, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7509787131" }, { "job": 80340077529, "time": 144, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7509855570" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/27210774283", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/pre", "_commit": { "id": "74a5498c555bc6507bc9b1731b0df1d6608edb49", "author": "Marjolein Fokkema ", "committer": "cran-robot ", "message": "version 1.0.9\n", "time": 1780998002 }, "_maintainer": { "name": "Marjolein Fokkema", "email": "m.fokkema@fsw.leidenuniv.nl", "login": "marjoleinf", "twitter": "@MarjoFokkema", "description": "Associate Professor of Methods & Statistics for Psychology", "uuid": 16515996 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "earth", "role": "Imports" }, { "package": "Formula", "role": "Imports" }, { "package": "glmnet", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "partykit", "version": ">= 1.2-0", "role": "Imports" }, { "package": "rpart", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "survival", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "MatrixModels", "role": "Imports" }, { "package": "interp", "role": "Suggests" }, { "package": "datasets", "role": "Suggests" }, { "package": "doParallel", "role": "Suggests" }, { "package": "foreach", "role": "Suggests" }, { "package": "glmertree", "role": "Suggests" }, { "package": "grid", "role": "Suggests" }, { "package": "mlbench", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "mboost", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "caret", "role": "Suggests" }, { "package": "pROC", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "mice", "role": "Suggests" }, { "package": "shape", "role": "Suggests" }, { "package": "randomForest", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 1, "_updates": [ { "week": "2025-36", "n": 1 }, { "week": "2026-24", "n": 1 } ], "_tags": [ { "name": "1.0.8", "date": "2025-09-06" }, { "name": "1.0.9", "date": "2026-06-09" } ], "_stars": 0, "_contributors": [ { "user": "marjoleinf", "count": 20, "uuid": 16515996 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 4955, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/pre" }, "_mentions": 3, "_devurl": "https://github.com/marjoleinf/pre", "_searchresults": 113, "_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/pre.html", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/marjoleinf/pre", "_realowner": "marjoleinf", "_cranurl": false, "_releases": [ { "version": "0.1", "date": "2016-12-24" }, { "version": "0.2", "date": "2017-04-26" }, { "version": "0.3.0", "date": "2017-08-03" }, { "version": "0.4", "date": "2017-08-31" }, { "version": "0.4-1", "date": "2018-01-23" }, { "version": "0.5.0", "date": "2018-05-07" }, { "version": "0.6.0", "date": "2018-08-03" }, { "version": "0.7.0", "date": "2019-04-01" }, { "version": "0.7.1", "date": "2019-04-26" }, { "version": "0.7.2", "date": "2019-11-29" }, { "version": "1.0.0", "date": "2020-03-06" }, { "version": "1.0.1", "date": "2021-07-05" }, { "version": "1.0.2", "date": "2022-02-25" }, { "version": "1.0.3", "date": "2022-03-12" }, { "version": "1.0.4", "date": "2022-03-30" }, { "version": "1.0.5", "date": "2022-06-10" }, { "version": "1.0.6", "date": "2023-02-13" }, { "version": "1.0.7", "date": "2024-01-12" }, { "version": "1.0.8", "date": "2025-09-06" }, { "version": "1.0.9", "date": "2026-06-09" } ], "_exports": [ "bsnullinteract", "caret_pre_model", "corplot", "cvpre", "eTerm", "explain", "gpe", "gpe_cv.glmnet", "gpe_earth", "gpe_linear", "gpe_rules_pre", "gpe_sample", "gpe_trees", "importance", "interact", "lTerm", "maxdepth_sampler", "mi_mean", "mi_pre", "pairplot", "pre", "prune_pre", "rare_level_sampler", "rTerm", "singleplot" ], "_datasets": [ { "name": "carrillo", "title": "Data on personality characteristics and depressive symptom severity", "object": "carrillo", "class": [ "data.frame" ], "fields": [ "n1", "n2", "n3", "n4", "n5", "n6", "ntot", "e1", "e2", "e3", "e4", "e5", "e6", "etot", "open1", "open2", "open3", "open4", "open6", "opentot", "altot", "contot", "bdi", "sexo", "edad", "open5" ], "rows": 112, "table": true, "tojson": true } ], "_help": [ { "page": "bsnullinteract", "title": "Compute bootstrapped null interaction prediction rule ensembles", "topics": [ "bsnullinteract" ] }, { "page": "caret_pre_model", "title": "Model set up for train function of package caret", "topics": [ "caret_pre_model" ] }, { "page": "carrillo", "title": "Data on personality characteristics and depressive symptom severity", "topics": [ "carrillo" ] }, { "page": "coef.gpe", "title": "Coefficients for a General Prediction Ensemble (gpe)", "topics": [ "coef.gpe" ] }, { "page": "coef.pre", "title": "Coefficients for the final prediction rule ensemble", "topics": [ "coef.pre" ] }, { "page": "corplot", "title": "Plot correlations between baselearners in a prediction rule ensemble (pre)", "topics": [ "corplot" ] }, { "page": "cvpre", "title": "Full k-fold cross validation of a prediction rule ensemble (pre)", "topics": [ "cvpre" ] }, { "page": "explain", "title": "Explain predictions from final prediction rule ensemble", "topics": [ "explain" ] }, { "page": "gpe", "title": "Derive a General Prediction Ensemble (gpe)", "topics": [ "gpe" ] }, { "page": "gpe_cv.glmnet", "title": "Default penalized trainer for gpe", "topics": [ "gpe_cv.glmnet" ] }, { "page": "gpe_rules_pre", "title": "Get rule learner for gpe which mimics behavior of pre", "topics": [ "gpe_rules_pre" ] }, { "page": "gpe_sample", "title": "Sampling Function Generator for gpe", "topics": [ "gpe_sample" ] }, { "page": "gpe_trees", "title": "Learner Functions Generators for gpe", "topics": [ "gpe_earth", "gpe_linear", "gpe_trees" ] }, { "page": "importance.pre", "title": "Calculate importances of baselearners and input variables in a prediction rule ensemble (pre)", "topics": [ "importance", "importance.pre" ] }, { "page": "interact", "title": "Calculate interaction statistics for variables in a prediction rule ensemble (pre)", "topics": [ "interact" ] }, { "page": "maxdepth_sampler", "title": "Sampling function generator for specifying varying maximum tree depth in a prediction rule ensemble (pre)", "topics": [ "maxdepth_sampler" ] }, { "page": "mi_mean", "title": "Compute the average dataset over imputed datasets.", "topics": [ "mi_mean" ] }, { "page": "mi_pre", "title": "Fit a prediction rule ensemble to multiply-imputed data (experimental)", "topics": [ "mi_pre" ] }, { "page": "pairplot", "title": "Create partial dependence plot for a pair of predictor variables in a prediction rule ensemble (pre)", "topics": [ "pairplot" ] }, { "page": "plot.pre", "title": "Plot method for class pre", "topics": [ "plot.pre" ] }, { "page": "pre", "title": "Derive a prediction rule ensemble", "topics": [ "pre" ] }, { "page": "predict.gpe", "title": "Predicted values based on gpe ensemble", "topics": [ "predict.gpe" ] }, { "page": "predict.pre", "title": "Predicted values based on final prediction rule ensemble", "topics": [ "predict.pre" ] }, { "page": "print.gpe", "title": "Print a General Prediction Ensemble (gpe)", "topics": [ "print.gpe" ] }, { "page": "print.pre", "title": "Print method for objects of class pre", "topics": [ "print.pre" ] }, { "page": "prune_pre", "title": "Get the optimal lambda and gamma parameter values for an ensemble of given size", "topics": [ "prune_pre" ] }, { "page": "rare_level_sampler", "title": "Dealing with rare factor levels in fitting prediction rule ensembles.", "topics": [ "rare_level_sampler" ] }, { "page": "rTerm", "title": "Wrapper Functions for terms in gpe", "topics": [ "eTerm", "lTerm", "rTerm" ] }, { "page": "singleplot", "title": "Create partial dependence plot for a single variable in a prediction rule ensemble (pre)", "topics": [ "singleplot" ] }, { "page": "summary.gpe", "title": "Summary method for a General Prediction Ensemble (gpe)", "topics": [ "summary.gpe" ] }, { "page": "summary.pre", "title": "Summary method for objects of class pre", "topics": [ "summary.pre" ] } ], "_readme": "https://github.com/cran/pre/raw/HEAD/README.md", "_rundeps": [ "cli", "codetools", "earth", "foreach", "Formula", "glmnet", "glue", "inum", "iterators", "lattice", "libcoin", "lifecycle", "magrittr", "Matrix", "MatrixModels", "mvtnorm", "partykit", "plotmo", "plotrix", "Rcpp", "RcppEigen", "rlang", "rpart", "shape", "stringi", "stringr", "survival", "vctrs" ], "_vignettes": [ { "source": "Missingness.Rmd", "filename": "Missingness.html", "title": "Dealing with missing data in fitting prediction rule ensembles", "author": "Marjolein Fokkema", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Example: Predicting wind speed", "Listwise Deletion", "Single Imputation", "Multiple Imputation", "Stacking imputed datasets", "Pooling", "Comparing accuracy and sparsity", "Session info", "References" ], "created": "2025-09-06 05:11:02", "modified": "2025-09-06 05:11:02", "commits": 1 }, { "source": "speed.Rmd", "filename": "speed.html", "title": "Speeding up computations", "author": "Marjolein Fokkema", "engine": "knitr::rmarkdown", "headings": [ "Faster rule generation", "Tree fitting algorithm", "Maximum rule depth", "Number of trees", "Faster estimation of the final ensemble", "Parallel computation", "Number of cross-validation folds", "References" ], "created": "2024-01-13 02:39:39", "modified": "2025-09-06 05:11:02", "commits": 2 }, { "source": "relaxed.Rmd", "filename": "relaxed.html", "title": "More adaptive or relaxed: Fitting sparser rule ensembles with relaxed and/or adaptive lasso", "author": "Marjolein Fokkema", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Relaxed lasso", "Adaptive lasso", "Relaxed adaptive lasso", "Example: Predicting Ozone levels with relaxed and/or adaptive lasso", "Forward stepwise selection with the relaxed lasso", "Conclusion", "Session info", "References" ], "created": "2022-03-30 06:00:12", "modified": "2026-06-09 09:40:02", "commits": 5 }, { "source": "Tuning.Rmd", "filename": "Tuning.html", "title": "Tuning parameters of function pre", "author": "Marjolein Fokkema", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Tuning parameters", "Tuning parameters using package caret", "Using Breiman's random forests for rule generation", "References" ], "created": "2025-09-06 05:11:02", "modified": "2026-06-09 09:40:02", "commits": 2 } ], "_score": 6.304424603072002, "_indexed": false, "_nocasepkg": "pre", "_universes": [ "cran" ], "_previous": "1.0.8", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.0.9", "date": "2026-06-09T13:56:53.000Z", "distro": "noble", "commit": "74a5498c555bc6507bc9b1731b0df1d6608edb49", "fileid": "e2ee38d554b4f6e5b77c581b0f39f612b7554ec8bbaaf20fa263ca6013a9a2c2", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/27210774283" }, { "r": "4.6.0", "os": "linux", "version": "1.0.9", "date": "2026-06-09T13:56:53.000Z", "distro": "noble", "commit": "74a5498c555bc6507bc9b1731b0df1d6608edb49", "fileid": "3d11276a4ebe06bd4576bb4cfaa0f93a12942bfda139ff5d9c2bd5a39f3deda0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/27210774283" }, { "r": "4.6.0", "os": "wasm", "version": "1.0.9", "date": "2026-06-09T13:56:58.000Z", "commit": "74a5498c555bc6507bc9b1731b0df1d6608edb49", "fileid": "dcc25bbeb2b939c035f94936931b3c623a86c45501b63fe42e528aaef5c01157", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/27210774283" } ] }