{ "_id": "6a215e18cd65a98ecbd2fa96", "Package": "collinear", "Title": "Automated Multicollinearity Management", "Version": "3.0.2", "Authors@R": "person(given = \"Blas M.\",\nfamily = \"Benito\", ,\nemail = \"blasbenito@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5105-7232\"))", "URL": "https://blasbenito.github.io/collinear/", "BugReports": "https://github.com/blasbenito/collinear/issues", "Description": "Provides a comprehensive and automated workflow for\nmanaging multicollinearity in data frames with numeric and/or\ncategorical variables. The package integrates five robust\nmethods into a single function: (1) target encoding of\ncategorical variables based on response values (Micci-Barreca,\n2001 (Micci-Barreca, D. 2001 ); (2)\nautomated feature prioritization to preserve key predictors\nduring filtering; (3 and 4) pairwise correlation and VIF\nfiltering across all variable types (numeric–numeric,\nnumeric–categorical, and categorical–categorical); (5) adaptive\ncorrelation and VIF thresholds. Together, these methods enable\na reliable multicollinearity management in most use cases while\nmaintaining model integrity. The package also supports parallel\nprocessing and progress tracking via the packages 'future' and\n'progressr', and provides seamless integration with the\n'tidymodels' ecosystem through a dedicated recipe step.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE, roclets = c(\"collate\", \"namespace\",\n\"rd\", \"roxyglobals::global_roclet\"))", "Config/testthat/edition": "3", "LazyData": "true", "LazyDataCompression": "xz", "Language": "en-US", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibicu-dev libssl-dev libproj-dev libsqlite3-dev\nlibudunits2-dev", "Repository": "https://blasbenito.r-universe.dev", "Date/Publication": "2026-06-04 06:19:51 UTC", "RemoteUrl": "https://github.com/blasbenito/collinear", "RemoteRef": "HEAD", "RemoteSha": "586acc5dd256c902771c5952343172eab341cd1a", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-04 11:07:02 UTC", "User": "root" }, "Author": "Blas M. Benito [aut, cre, cph] (ORCID:\n)", "Maintainer": "Blas M. Benito ", "MD5sum": "3500e8392223cf15fd650b8a8edbf2d6", "_user": "blasbenito", "_type": "src", "_file": "collinear_3.0.2.tar.gz", "_fileid": "88c545286a604c27f24103e97ab3b8dbda61c87b67d3d203387ccfce400eb41c", "_filesize": 813269, "_sha256": "88c545286a604c27f24103e97ab3b8dbda61c87b67d3d203387ccfce400eb41c", "_created": "2026-06-04T11:07:02.000Z", "_published": "2026-06-04T11:14:32.084Z", "_distro": "noble", "_jobs": [ { "job": 79505588135, "time": 195, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7409902540" }, { "job": 79505588140, "time": 190, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7409900925" }, { "job": 79505588142, "time": 119, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7409917423" }, { "job": 79505588221, "time": 117, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7409967909" }, { "job": 79504988576, "time": 225, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7409839059" }, { "job": 79505588245, "time": 136, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7409882741" }, { "job": 79505588153, "time": 142, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7409884998" }, { "job": 79505588256, "time": 153, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7409888721" }, { "job": 79505588137, "time": 178, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7409897977" } ], "_buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/blasbenito/collinear", "_commit": { "id": "586acc5dd256c902771c5952343172eab341cd1a", "author": "Blas M. Benito ", "committer": "Blas M. Benito ", "message": "- fixed typo in NEWS.Md\n- prepared cran-comments.md\n", "time": 1780553991 }, "_maintainer": { "name": "Blas M. Benito", "email": "blasbenito@gmail.com", "login": "blasbenito", "mastodon": "@blasbenito@fosstodon.org", "bluesky": "@blasbenito.com", "orcid": "0000-0001-5105-7232", "description": "🌍 Senior Spatial Data Scientist | Geospatial Tech Lead | 🌱 AgTech | PhD in Computational Ecology + MSc in GIS | rstats developer", "uuid": 5610068 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "progressr", "role": "Imports" }, { "package": "future.apply", "role": "Imports" }, { "package": "mgcv", "role": "Imports" }, { "package": "ranger", "role": "Imports" }, { "package": "recipes", "version": ">= 1.0.9", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "spatialData", "role": "Imports" }, { "package": "future", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "spelling", "role": "Suggests" } ], "_owner": "blasbenito", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-33", "n": 4 }, { "week": "2025-34", "n": 5 }, { "week": "2025-35", "n": 2 }, { "week": "2025-36", "n": 9 }, { "week": "2025-37", "n": 5 }, { "week": "2025-38", "n": 10 }, { "week": "2025-39", "n": 8 }, { "week": "2025-40", "n": 1 }, { "week": "2025-45", "n": 1 }, { "week": "2025-46", "n": 4 }, { "week": "2025-47", "n": 1 }, { "week": "2025-48", "n": 8 }, { "week": "2025-49", "n": 19 }, { "week": "2025-50", "n": 7 }, { "week": "2025-51", "n": 1 }, { "week": "2026-01", "n": 1 }, { "week": "2026-02", "n": 1 }, { "week": "2026-12", "n": 1 }, { "week": "2026-19", "n": 11 }, { "week": "2026-20", "n": 2 }, { "week": "2026-22", "n": 4 }, { "week": "2026-23", "n": 4 } ], "_tags": [ { "name": "v3.0.0", "date": "2025-12-06" }, { "name": "v3.0.1", "date": "2026-05-07" } ], "_topics": [ "machine-learning", "multicollinearity", "statistics" ], "_stars": 18, "_contributors": [ { "user": "blasbenito", "count": 262, "uuid": 5610068 }, { "user": "ambarbosa", "count": 2, "uuid": 12085508 } ], "_userbio": { "uuid": 5610068, "type": "user", "name": "Blas Benito", "description": "🌍 Senior Spatial Data Scientist | Geospatial Tech Lead | 🌱 AgTech | PhD in Computational Ecology + MSc in GIS | rstats developer" }, "_downloads": { "count": 446, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/collinear" }, "_devurl": "https://github.com/blasbenito/collinear", "_pkgdown": "https://blasbenito.github.io/collinear/", "_searchresults": 31, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/collinear.html", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/blasbenito/collinear", "_realowner": "blasbenito", "_cranurl": true, "_releases": [ { "version": "1.0.1", "date": "2023-10-24" }, { "version": "1.1.0", "date": "2023-11-30" }, { "version": "1.1.1", "date": "2023-12-08" }, { "version": "2.0.0", "date": "2024-11-08" }, { "version": "3.0.0", "date": "2025-12-11" }, { "version": "3.0.1", "date": "2026-05-07" }, { "version": "3.0.2", "date": "2026-06-04" } ], "_exports": [ "case_weights", "collinear", "collinear_select", "collinear_stats", "cor_clusters", "cor_cramer", "cor_df", "cor_matrix", "cor_select", "cor_stats", "drop_geometry_column", "f_auto", "f_auto_rules", "f_binomial_gam", "f_binomial_glm", "f_binomial_rf", "f_categorical_rf", "f_count_gam", "f_count_glm", "f_count_rf", "f_functions", "f_numeric_gam", "f_numeric_glm", "f_numeric_rf", "identify_categorical_variables", "identify_logical_variables", "identify_numeric_variables", "identify_response_type", "identify_valid_variables", "identify_zero_variance_variables", "model_formula", "preference_order", "score_auc", "score_cramer", "score_r2", "step_collinear", "target_encoding_lab", "target_encoding_loo", "target_encoding_mean", "target_encoding_rank", "validate_arg_df", "validate_arg_df_not_null", "validate_arg_encoding_method", "validate_arg_f", "validate_arg_function_name", "validate_arg_max_cor", "validate_arg_max_vif", "validate_arg_predictors", "validate_arg_preference_order", "validate_arg_quiet", "validate_arg_responses", "vif", "vif_df", "vif_select", "vif_stats" ], "_datasets": [ { "name": "experiment_adaptive_thresholds", "title": "Dataframe resulting from experiment to test the automatic selection of multicollinearity thresholds", "object": "experiment_adaptive_thresholds", "class": [ "data.frame" ], "fields": [ "input_rows", "input_predictors", "output_predictors", "input_cor_q75", "output_cor_q75", "input_cor_max", "output_cor_max", "input_vif_max", "output_vif_max" ], "rows": 10000, "table": true, "tojson": true }, { "name": "experiment_cor_vs_vif", "title": "Dataframe with results of experiment comparing correlation and VIF thresholds", "object": "experiment_cor_vs_vif", "class": [ "data.frame" ], "fields": [ "input_rows", "input_predictors", "output_predictors", "max_cor", "max_vif", "out_selection_jaccard" ], "rows": 10000, "table": true, "tojson": true }, { "name": "gam_cor_to_vif", "title": "GAM describing the relationship between correlation and VIF thresholds", "object": "gam_cor_to_vif", "class": [ "gam", "glm", "lm" ], "fields": [], "table": false, "tojson": false }, { "name": "prediction_cor_to_vif", "title": "Prediction of the model 'gam_cor_to_vif' across correlation values", "object": "prediction_cor_to_vif", "class": [ "data.frame" ], "fields": [ "max_cor", "max_vif" ], "rows": 901, "table": true, "tojson": false }, { "name": "toy", "title": "Toy dataframe with varying levels of multicollinearity", "object": "toy", "class": [ "data.frame" ], "fields": [ "y", "a", "b", "c", "d" ], "rows": 2000, "table": true, "tojson": true } ], "_help": [ { "page": "case_weights", "title": "Generate sample weights for imbalanced responses", "concept": [ "modelling_tools" ], "topics": [ "case_weights" ] }, { "page": "collinear", "title": "Smart multicollinearity management", "concept": [ "multicollinearity_filtering" ], "topics": [ "collinear" ] }, { "page": "collinear_select", "title": "Dual multicollinearity filtering algorithm", "concept": [ "multicollinearity_filtering" ], "topics": [ "collinear_select" ] }, { "page": "collinear_stats", "title": "Compute summary statistics for correlation and VIF", "concept": [ "multicollinearity_assessment" ], "topics": [ "collinear_stats" ] }, { "page": "cor_clusters", "title": "Group predictors by hierarchical correlation clustering", "concept": [ "multicollinearity_assessment" ], "topics": [ "cor_clusters" ] }, { "page": "cor_cramer", "title": "Quantify association between categorical variables", "concept": [ "multicollinearity_assessment" ], "topics": [ "cor_cramer" ] }, { "page": "cor_df", "title": "Compute signed pairwise correlations dataframe", "concept": [ "multicollinearity_assessment" ], "topics": [ "cor_df" ] }, { "page": "cor_matrix", "title": "Signed pairwise correlation matrix", "concept": [ "multicollinearity_assessment" ], "topics": [ "cor_matrix" ] }, { "page": "cor_select", "title": "Multicollinearity filtering by pairwise correlation threshold", "concept": [ "multicollinearity_filtering" ], "topics": [ "cor_select" ] }, { "page": "cor_stats", "title": "Compute summary statistics for absolute pairwise correlations", "concept": [ "multicollinearity_assessment" ], "topics": [ "cor_stats" ] }, { "page": "drop_geometry_column", "title": "Removes 'geometry' Column From 'sf' Dataframes", "concept": [ "argument_validation" ], "topics": [ "drop_geometry_column" ] }, { "page": "experiment_adaptive_thresholds", "title": "Dataframe resulting from experiment to test the automatic selection of multicollinearity thresholds", "concept": [ "experiments" ], "topics": [ "experiment_adaptive_thresholds" ] }, { "page": "experiment_cor_vs_vif", "title": "Dataframe with results of experiment comparing correlation and VIF thresholds", "concept": [ "experiments" ], "topics": [ "experiment_cor_vs_vif" ] }, { "page": "f_auto", "title": "Automatic selection of predictor scoring method", "concept": [ "preference_order_tools" ], "topics": [ "f_auto" ] }, { "page": "f_auto_rules", "title": "Decision rules for 'f_auto()'", "concept": [ "preference_order_tools" ], "topics": [ "f_auto_rules" ] }, { "page": "f_binomial_gam", "title": "Area under the curve of binomial GAM predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_binomial_gam" ] }, { "page": "f_binomial_glm", "title": "Area Under the Curve of Binomial GLM predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_binomial_glm" ] }, { "page": "f_binomial_rf", "title": "Area Under the Curve of Binomial Random Forest predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_binomial_rf" ] }, { "page": "f_categorical_rf", "title": "Cramer's V of Categorical Random Forest predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_categorical_rf" ] }, { "page": "f_count_gam", "title": "R-squared of Poisson GAM predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_count_gam" ] }, { "page": "f_count_glm", "title": "R-squared of Poisson GLM predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_count_glm" ] }, { "page": "f_count_rf", "title": "R-squared of Random Forest predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_count_rf" ] }, { "page": "f_functions", "title": "List predictor scoring functions", "concept": [ "preference_order_tools" ], "topics": [ "f_functions" ] }, { "page": "f_numeric_gam", "title": "R-squared of Gaussian GAM predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_numeric_gam" ] }, { "page": "f_numeric_glm", "title": "R-squared of Gaussian GLM predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_numeric_glm" ] }, { "page": "f_numeric_rf", "title": "R-squared of Random Forest predictions vs. observations", "concept": [ "preference_order_functions" ], "topics": [ "f_numeric_rf" ] }, { "page": "gam_cor_to_vif", "title": "GAM describing the relationship between correlation and VIF thresholds", "concept": [ "experiments" ], "topics": [ "gam_cor_to_vif" ] }, { "page": "identify_categorical_variables", "title": "Find valid categorical variables in a dataframe", "concept": [ "data_types" ], "topics": [ "identify_categorical_variables" ] }, { "page": "identify_logical_variables", "title": "Find logical variables in a dataframe", "concept": [ "data_types" ], "topics": [ "identify_logical_variables" ] }, { "page": "identify_numeric_variables", "title": "Find valid numeric variables in a dataframe", "concept": [ "data_types" ], "topics": [ "identify_numeric_variables" ] }, { "page": "identify_response_type", "title": "Detect response variable type for model selection", "concept": [ "data_types" ], "topics": [ "identify_response_type" ] }, { "page": "identify_valid_variables", "title": "Find valid numeric, categorical, and logical variables in a dataframe", "concept": [ "data_types" ], "topics": [ "identify_valid_variables" ] }, { "page": "identify_zero_variance_variables", "title": "Find near-zero variance variables in a dataframe", "concept": [ "data_types" ], "topics": [ "identify_zero_variance_variables" ] }, { "page": "model_formula", "title": "Build model formulas from response and predictors", "concept": [ "modelling_tools" ], "topics": [ "model_formula" ] }, { "page": "prediction_cor_to_vif", "title": "Prediction of the model 'gam_cor_to_vif' across correlation values", "concept": [ "experiments" ], "topics": [ "prediction_cor_to_vif" ] }, { "page": "preference_order", "title": "Rank predictors by importance or multicollinearity", "concept": [ "preference_order_functions" ], "topics": [ "preference_order" ] }, { "page": "print.collinear_output", "title": "Print all collinear selection results of 'collinear()'", "concept": [ "S3_methods" ], "topics": [ "print.collinear_output" ] }, { "page": "print.collinear_selection", "title": "Print single selection results from 'collinear'", "concept": [ "S3_methods" ], "topics": [ "print.collinear_selection" ] }, { "page": "score_auc", "title": "Compute area under the ROC curve between binomial observations and probabilistic predictions", "concept": [ "modelling_tools" ], "topics": [ "score_auc" ] }, { "page": "score_cramer", "title": "Compute Cramer's V between categorical observations and predictions", "concept": [ "modelling_tools" ], "topics": [ "score_cramer" ] }, { "page": "score_r2", "title": "Compute R-squared between numeric observations and predictions", "concept": [ "modelling_tools" ], "topics": [ "score_r2" ] }, { "page": "step_collinear", "title": "Tidymodels recipe step for multicollinearity filtering", "concept": [ "multicollinearity_filtering" ], "topics": [ "bake.step_collinear", "prep.step_collinear", "step_collinear" ] }, { "page": "summary.collinear_output", "title": "Summarize all results of 'collinear()'", "concept": [ "S3_methods" ], "topics": [ "summary.collinear_output" ] }, { "page": "summary.collinear_selection", "title": "Summarize single response selection results of 'collinear'", "concept": [ "S3_methods" ], "topics": [ "summary.collinear_selection" ] }, { "page": "target_encoding_lab", "title": "Convert categorical predictors to numeric via target encoding", "concept": [ "target_encoding" ], "topics": [ "target_encoding_lab" ] }, { "page": "target_encoding_methods", "title": "Encode categories as response means", "concept": [ "target_encoding" ], "topics": [ "target_encoding_loo", "target_encoding_mean", "target_encoding_rank" ] }, { "page": "toy", "title": "Toy dataframe with varying levels of multicollinearity", "concept": [ "example_data" ], "topics": [ "toy" ] }, { "page": "validate_arg_df", "title": "Check and prepare argument 'df'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_df" ] }, { "page": "validate_arg_df_not_null", "title": "Ensure that argument 'df' is not 'NULL'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_df_not_null" ] }, { "page": "validate_arg_encoding_method", "title": "Check and validate argument 'encoding_method'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_encoding_method" ] }, { "page": "validate_arg_f", "title": "Check and validate argument 'f'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_f" ] }, { "page": "validate_arg_function_name", "title": "Build hierarchical function names for messages", "concept": [ "argument_validation" ], "topics": [ "validate_arg_function_name" ] }, { "page": "validate_arg_max_cor", "title": "Check and constrain argument 'max_cor'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_max_cor" ] }, { "page": "validate_arg_max_vif", "title": "Check and constrain argument 'max_vif'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_max_vif" ] }, { "page": "validate_arg_predictors", "title": "Check and validate argument 'predictors'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_predictors" ] }, { "page": "validate_arg_preference_order", "title": "Check and complete argument 'preference_order'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_preference_order" ] }, { "page": "validate_arg_quiet", "title": "Check and validate argument 'quiet'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_quiet" ] }, { "page": "validate_arg_responses", "title": "Check and validate arguments 'response' and 'responses'", "concept": [ "argument_validation" ], "topics": [ "validate_arg_responses" ] }, { "page": "vif", "title": "Compute variance inflation factors from a correlation matrix", "concept": [ "multicollinearity_assessment" ], "topics": [ "vif" ] }, { "page": "vif_df", "title": "Compute variance inflation factors dataframe", "concept": [ "multicollinearity_assessment" ], "topics": [ "vif_df" ] }, { "page": "vif_select", "title": "Multicollinearity filtering by variance inflation factor threshold", "concept": [ "multicollinearity_filtering" ], "topics": [ "vif_select" ] }, { "page": "vif_stats", "title": "VIF Statistics", "concept": [ "multicollinearity_assessment" ], "topics": [ "vif_stats" ] } ], "_pkglogo": "https://github.com/blasbenito/collinear/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/blasbenito/collinear/raw/HEAD/README.md", "_rundeps": [ "class", "classInt", "cli", "clock", "codetools", "cpp11", "data.table", "DBI", "diagram", "digest", "dplyr", "e1071", "farver", "future", "future.apply", "generics", "ggplot2", "globals", "glue", "gower", "gtable", "hardhat", "ipred", "isoband", "KernSmooth", "labeling", "lattice", "lava", "lifecycle", "listenv", "lubridate", "magrittr", "MASS", "Matrix", "mgcv", "nlme", "nnet", "numDeriv", "parallelly", "pillar", "pkgconfig", "prodlim", "progressr", "proxy", "purrr", "R6", "ranger", "RColorBrewer", "Rcpp", "RcppEigen", "recipes", "rlang", "rpart", "s2", "S7", "scales", "sf", "shape", "sparsevctrs", "spatialData", "SQUAREM", "stringi", "stringr", "survival", "terra", "tibble", "tidyr", "tidyselect", "timechange", "timeDate", "tzdb", "units", "utf8", "vctrs", "viridisLite", "withr", "wk" ], "_score": 5.788026884095803, "_indexed": true, "_nocasepkg": "collinear", "_universes": [ "blasbenito" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "3.0.2", "date": "2026-06-04T11:09:40.000Z", "distro": "noble", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "f469bcd62eaad1f3a618f0d883fdf8d14bae8800ed71469c756895b8cfdb5035", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.6.0", "os": "linux", "version": "3.0.2", "date": "2026-06-04T11:09:38.000Z", "distro": "noble", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "aaa93d04e0f403f81505cc2547286317d5796be1863720acc2d8c41eb78df5f1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.5.3", "os": "mac", "version": "3.0.2", "date": "2026-06-04T11:10:51.000Z", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "dbe9971c78c306982aec9a0ec8052c55d82bf748147c01ffae64ad8c6351a057", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.6.0", "os": "mac", "version": "3.0.2", "date": "2026-06-04T11:13:29.000Z", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "3a9466a7e73e40276ba0981aa941d0e55ea6e0090975c94ca7b298635338287d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.6.0", "os": "wasm", "version": "3.0.2", "date": "2026-06-04T11:09:44.000Z", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "1d74ebc0a0cccf619aeebeee89137de73c1ec7546a7763897fbdb769e90e18f1", "status": "success", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.7.0", "os": "win", "version": "3.0.2", "date": "2026-06-04T11:08:33.000Z", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "c1437cbe8e2bcb9426e3fd9fbcfea525ae5fc90dc464c51547516e5ad80a1fc0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.5.3", "os": "win", "version": "3.0.2", "date": "2026-06-04T11:08:45.000Z", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "3d4fa1115d3ace6ee40e936de97a0a6971f329f772f53af728c0ca60a952af05", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" }, { "r": "4.6.0", "os": "win", "version": "3.0.2", "date": "2026-06-04T11:09:13.000Z", "commit": "586acc5dd256c902771c5952343172eab341cd1a", "fileid": "4a398f756999989d07bac5cba4e9794e156f287f81ccba0489f0d51b0d02210a", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/blasbenito/actions/runs/26947769737" } ] }