{ "_id": "6acb5b0ffe5cc25786e8ea06", "Package": "ppsr", "Type": "Package", "Title": "Predictive Power Score", "Version": "0.0.5", "Authors@R": "person(\"Paul\", \"van der Laken\", email = \"paulvanderlaken@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"))", "Description": "The Predictive Power Score (PPS) is an asymmetric,\ndata-type-agnostic score that can detect linear or non-linear\nrelationships between two variables. The score ranges from 0\n(no predictive power) to 1 (perfect predictive power). PPS can\nbe useful for data exploration purposes, in the same way\ncorrelation analysis is. For more information on PPS, see\n.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Config/testthat/edition": "3", "Config/testthat/parallel": "true", "RoxygenNote": "7.2.3", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://paulvanderlaken.r-universe.dev", "Date/Publication": "2024-02-18 12:45:20 UTC", "RemoteUrl": "https://github.com/paulvanderlaken/ppsr", "RemoteRef": "HEAD", "RemoteSha": "8245f33568ff8854545a0e290b7d59558a9a413f", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-11 09:26:14 UTC", "User": "root" }, "Author": "Paul van der Laken [aut, cre, cph]", "Maintainer": "Paul van der Laken ", "_user": "paulvanderlaken", "_type": "src", "_file": "ppsr_0.0.5.tar.gz", "_fileid": "https://r2.ropensci.org/d30572e8eac78ed8a910f55e9178a61fa6f11bba5e1a5050ed0ce7f3022bfe44", "_filesize": 477856, "_sha256": "d30572e8eac78ed8a910f55e9178a61fa6f11bba5e1a5050ed0ce7f3022bfe44", "_expires": "2027-01-19T09:46:53.000Z", "_created": "2026-10-11T09:26:14.000Z", "_published": 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], "_homeurl": "https://github.com/paulvanderlaken/ppsr", "_realowner": "paulvanderlaken", "_cranurl": true, "_releases": [ { "version": "0.0.2", "date": "2021-03-02" }, { "version": "0.0.5", "date": "2024-02-18" } ], "_exports": [ "available_algorithms", "available_evaluation_metrics", "score", "score_correlations", "score_df", "score_matrix", "score_predictors", "visualize_both", "visualize_correlations", "visualize_pps" ], "_help": [ { "page": "available_algorithms", "title": "Lists all algorithms currently supported", "topics": [ "available_algorithms" ] }, { "page": "available_evaluation_metrics", "title": "Lists all evaluation metrics currently supported", "topics": [ "available_evaluation_metrics" ] }, { "page": "normalize_score", "title": "Normalizes the original score compared to a naive baseline score The calculation that's being performed depends on the type of model", "topics": [ "normalize_score" ] }, { "page": "ppsr", "title": "ppsr: An R implementation of the Predictive Power Score (PPS)", "topics": [ "ppsr-package", "ppsr" ] }, { "page": "score", "title": "Calculate predictive power score for x on y", "topics": [ "score" ] }, { "page": "score_correlations", "title": "Calculate correlation coefficients for whole dataframe", "topics": [ "score_correlations" ] }, { "page": "score_df", "title": "Calculate predictive power scores for whole dataframe Iterates through the columns of the dataframe, calculating the predictive power score for every possible combination of 'x' and 'y'.", "topics": [ "score_df" ] }, { "page": "score_matrix", "title": "Calculate predictive power score matrix Iterates through the columns of the dataset, calculating the predictive power score for every possible combination of 'x' and 'y'.", "topics": [ "score_matrix" ] }, { "page": "score_model", "title": "Calculates out-of-sample model performance of a statistical model", "topics": [ "score_model" ] }, { "page": "score_naive", "title": "Calculate out-of-sample model performance of naive baseline model The calculation that's being performed depends on the type of model For regression models, the mean is used as prediction For classification, a model predicting random values and a model predicting modal values are used and the best model is taken as baseline score", "topics": [ "score_naive" ] }, { "page": "score_predictors", "title": "Calculate predictive power scores for y Calculates the predictive power scores for the specified 'y' variable using every column in the dataset as 'x', including itself.", "topics": [ "score_predictors" ] }, { "page": "visualize_both", "title": "Visualize the PPS & correlation matrices", "topics": [ "visualize_both" ] }, { "page": "visualize_correlations", "title": "Visualize the correlation matrix", "topics": [ "visualize_correlations" ] }, { "page": "visualize_pps", "title": "Visualize the Predictive Power scores of the entire dataframe, or given a target", "topics": [ "visualize_pps" ] } ], "_readme": 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