{ "_id": "6aaa7addea78b3e62eacd02c", "Package": "YARF", "Type": "Package", "Title": "Yet Another Random Forests Package", "Version": "1.2.0", "Authors@R": "c(\nperson(\"Adam\", \"Kapelner\", email = \"kapelner@qc.cuny.edu\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5985-6792\")),\nperson(\"Matt\", \"Olson\", role = \"aut\"),\nperson(\"Abhinav\", \"Patil\", role = \"aut\")\n)", "Description": "A customizable, asynchronous, and parallelized random\nforest implementation. Supports user-defined splitting and\naggregation behavior, missingness incorporated in attributes,\nand random-forest-based imputation.", "License": "GPL-3", "URL": "https://github.com/kapelner/YARF, https://kapelner.github.io/YARF/", "BugReports": "https://github.com/kapelner/YARF/issues", "VignetteBuilder": "knitr", "SystemRequirements": "Java (>= 1.8.0)", "Encoding": "UTF-8", "Config/roxygen2/version": "8.0.0.9000", "Config/testthat/edition": "3", "Config/Needs/website": "pkgdown", "Config/pak/sysreqs": "make default-jdk libicu-dev libx11-dev", "Repository": "https://kapelner.r-universe.dev", "Date/Publication": "2026-09-03 13:11:24 UTC", "RemoteUrl": "https://github.com/kapelner/YARF", "RemoteRef": "HEAD", "RemoteSha": "6677cdf14228609a67a334a7ace6f0792b1526c6", "RemoteSubdir": "YARF", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-16 11:14:14 UTC", "User": "root" }, "Author": "Adam Kapelner [aut, cre, cph] (ORCID:\n),\nMatt Olson [aut],\nAbhinav Patil [aut]", "Maintainer": "Adam Kapelner ", "_user": "kapelner", "_type": "src", "_file": "YARF_1.2.0.tar.gz", "_fileid": "https://r2.ropensci.org/33f98e1fd4a56d8eb1c0681f9d8ba49b735437c3d61e3bcf5671108351b38747", "_filesize": 715101, "_sha256": "33f98e1fd4a56d8eb1c0681f9d8ba49b735437c3d61e3bcf5671108351b38747", "_expires": "2026-12-25T11:17:47.000Z", "_created": "2026-09-16T11:14:14.000Z", "_published": "2026-09-16T11:17:48.948Z", "_jobs": [ { "job": 104771399705, "time": 171, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "10443383373" }, { "job": 104771399728, "time": 168, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "10443463235" }, { "job": 104771399725, "time": 117, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "10443552726" }, { "job": 104771399814, "time": 142, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "10443911038" }, { "job": 104770438262, "time": 198, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "10443975238" }, { "job": 104771399716, "time": 137, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "10443916070" }, { "job": 104771399778, "time": 105, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "10443765989" }, { "job": 104771399815, "time": 105, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "NOTE", "artifact": "10443428179" }, { "job": 104771399873, "time": 103, "config": "windows-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "10442999945" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/kapelner/actions/runs/35088938522", "_status": "success", "_upstream": "https://github.com/kapelner/YARF", "_commit": { "id": "6677cdf14228609a67a334a7ace6f0792b1526c6", "author": "Adam Kapelner ", "committer": "Adam Kapelner ", "message": "Bump GitHub Actions to Node 24 majors\n\nPages actions (configure-pages, upload-pages-artifact, deploy-pages) and\ncheckout moved to the releases that run on Node 24, removing the Node 20\ndeprecation annotations.\n\nCo-Authored-By: Claude Fable 5.1 \nClaude-Session: https://claude.ai/code/session_01WzPCnhBGK5VapkXTbqDiSL\n", "time": 1788441084 }, "_maintainer": { "name": "Adam Kapelner", "email": "kapelner@qc.cuny.edu", "login": "kapelner", "description": "I'm an Assoc. 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"p5.pi.doner", "p5.pi.acceptor", "p5.polarisable", "p5.sigma", "p5.branch", "p6.polar", "p6.size", "p6.flex", "p6.h.doner", "p6.h.acceptor", "p6.pi.doner", "p6.pi.acceptor", "p6.polarisable", "p6.sigma", "p6.branch", "activity" ], "rows": 186, "table": true, "tojson": true }, { "name": "wine.red", "title": "Benchmark regression datasets", "object": "benchmark_datasets", "file": "benchmark_datasets.RData", "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 }, { "name": "wine.white", "title": "Benchmark regression datasets", "object": "benchmark_datasets", "file": "benchmark_datasets.RData", "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": 4898, "table": true, "tojson": true } ], "_help": [ { "page": "automobile", "title": "Automobile data", "topics": [ "automobile" ] }, { "page": "benchmark_datasets", "title": "Benchmark regression datasets", "topics": [ "ankara", "baseball", "benchmark_datasets", "boston", "compactiv", "ozone", "pole", "triazine", "wine.red", "wine.white" ] }, { "page": "calcAUC", "title": "Calculates the area under the curve", "topics": [ "calcAUC" ] }, { "page": "compute_raw_proximity_info", "title": "Computes information about the \"proximity\" of observations within the YARF model. Given two datasets, information is computed for all pairs of observations. Information returned is the prediction nodes (for all trees) for both objects and their common node in the tree structure plus much information about the nodes.", "topics": [ "compute_raw_proximity_info" ] }, { "page": "cov_importance_test", "title": "Tests the effect of H0: one, or two, ... or all covariates are not predictive (out of sample) via a permutation test. We permute the covariates column(s) and build YARF models 'num_permutation_samples' times. The p-val is determined by a permutation-like test.", "topics": [ "cov_importance_test" ] }, { "page": "first_order_interaction_investigator", "title": "Investigate First Order Interactions", "topics": [ "first_order_interaction_investigator" ] }, { "page": "get_tree_num_nodes_leaves_max_depths", "title": "Gets information about each tree: the number of nodes, number of leaves and maximum depth.", "topics": [ "get_tree_num_nodes_leaves_max_depths" ] }, { "page": "illustrate_trees", "title": "Illustrates trees to PNG files in the local directory.", "topics": [ "illustrate_trees" ] }, { "page": "model_fit_test", "title": "Convenience method for testing a model's fit. The strategy is simple: one builds a YARF model from the residuals of the model you wish to fit and performs an omnibus test of all covariates against those residuals. This effecticely answers the question: \"is there out-of-sample predictive information left over after this model was fit?\" The p-val is determined by a permutation-like test.", "topics": [ "model_fit_test" ] }, { "page": "predict.YARF", "title": "Computes a response prediction for new data observation(s).", "topics": [ "predict.YARF" ] }, { "page": "prediction_nodes", "title": "Returns the leaf nodes that are responsible for predictions for a given dataset.", "topics": [ "prediction_nodes" ] }, { "page": "print.YARF", "title": "Alias for summary.", "topics": [ "print.YARF" ] }, { "page": "proximity_info", "title": "Computes information about the \"proximity\" of observations within the YARF model. Given two datasets, information is computed for all pairs of observation.", "topics": [ "proximity_info" ] }, { "page": "prune_YARF_model", "title": "Prunes the trees in a YARF mode. This is an irreversible modification to the tree structure.", "topics": [ "prune_YARF_model" ] }, { "page": "query_variable_counts", "title": "Function which queries the YARF model about how many times the variables are used when splitting", "topics": [ "query_variable_counts" ] }, { "page": "query_variable_proportions_across_trees", "title": "Function which queries the YARF model about the proportion of times the variables are used when splitting. This is a convenience method which merely normalizes the result of 'query_variable_counts'.", "topics": [ "query_variable_proportions_across_trees" ] }, { "page": "set_YARF_num_cores", "title": "Sets the number of cores for YARF to use for all operations which are multithreaded (e.g. model construction, prediction, etc)", "topics": [ "set_YARF_num_cores" ] }, { "page": "shared_initial_substring", "title": "Returns the shared initial portion of a string", "topics": [ "shared_initial_substring" ] }, { "page": "summary.YARF", "title": "Prints a summary of the model to the console", "topics": [ "summary.YARF" ] }, { "page": "tree_average_proximity_info", "title": "Computes information about the \"proximity\" of observations within the YARF model. Given two datasets, information is computed for all pairs of observations. Information is averaged over the trees in the model.", "topics": [ "tree_average_proximity_info" ] }, { "page": "YARF", "title": "Builds a YARF Model. There are many customizations available.", "topics": [ "YARF" ] }, { "page": "YARF_all_oob_results_matrix", "title": "All OOB Results Matrix", "topics": [ "YARF_all_oob_results_matrix" ] }, { "page": "YARF_convergence", "title": "Plot YARF convergence", "topics": [ "YARF_convergence" ] }, { "page": "yarf_duplicate", "title": "Duplicates the settings of YARF model. Wait is 'TRUE', all verbose logging is turned off and serializing is turned off as well.", "topics": [ "yarf_duplicate" ] }, { "page": "YARF_predict_all_trees", "title": "Computes a response prediction for new data observation(s) for all trees (unaggregated).", "topics": [ "YARF_predict_all_trees" ] }, { "page": "YARF_progress", "title": "Prints out a message reflecting the progress of the YARF model construction", "topics": [ "YARF_progress" ] }, { "page": "YARF_serialize", "title": "Serializes the model so the user can use 'save' and 'save.image' to write it to a file that can be then loaded into another and/or future R session.", "topics": [ "YARF_serialize" ] }, { "page": "YARF_set_aggregation_method", "title": "Sets the Tree Aggregation Method", "topics": [ "YARF_set_aggregation_method" ] }, { "page": "YARF_stop", "title": "Halts the model building.", "topics": [ "YARF_stop" ] }, { "page": "YARF_update_with_oob_results", "title": "Computes the out-of-bag (OOB) predictions for the training data. This gives a good sense of out-of-sample performance in the future.", "topics": [ "YARF_update_with_oob_results" ] }, { "page": "YARF_update_with_oob_test_results", "title": "Computes the out-of-bag (OOB) predictions for the test subset of the training data. This function should only be run once. If it run again you risk adapting your model to the test data which will lead to overfitting and hence a biased out-of-sample performance estimate.", "topics": [ "YARF_update_with_oob_test_results" ] }, { "page": "YARF_update_with_oob_validation_results", "title": "Computes the out-of-bag (OOB) predictions for the validation subset of the training data. This allows the user to try many different models while still leaving test data for true out-of-sample performance estimation.", "topics": [ "YARF_update_with_oob_validation_results" ] }, { "page": "YARFBAG", "title": "A convenience method to build a Bagged Tree model via YARF.", "topics": [ "YARFBAG" ] }, { "page": "YARFCART", "title": "A convenience method to build a CART model via YARF. 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