{ "_id": "6ad883d5d556c3a3418a886a", "Package": "modeldata", "Title": "Data Sets Useful for Modeling Examples", "Version": "1.6.0.9000", "Authors@R": "c(\nperson(\"Max\", \"Kuhn\", , \"max@posit.co\", role = c(\"aut\", \"cre\")),\nperson(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"),\ncomment = c(ROR = \"03wc8by49\"))\n)", "Description": "Data sets used for demonstrating or testing model-related\npackages are contained in this package.", "License": "MIT + file LICENSE", "URL": "https://modeldata.tidymodels.org,\nhttps://github.com/tidymodels/modeldata", "BugReports": "https://github.com/tidymodels/modeldata/issues", "Config/Needs/website": "tidyverse/tidytemplate, tidymodels/tidymodels", "Config/testthat/edition": "3", "Config/usethis/last-upkeep": "2025-04-27", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "Roxygen": "list(markdown = TRUE)", "Config/roxygen2/version": "8.0.0", "Repository": "https://tidymodels.r-universe.dev", "Date/Publication": "2026-08-22 15:39:04 UTC", "RemoteUrl": "https://github.com/tidymodels/modeldata", "RemoteRef": "HEAD", "RemoteSha": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-21 09:17:07 UTC", "User": "root" }, "Author": "Max Kuhn [aut, cre],\nPosit Software, PBC [cph, fnd] (ROR: )", "Maintainer": "Max Kuhn ", "_user": "tidymodels", "_type": "src", "_file": "modeldata_1.6.0.9000.tar.gz", "_fileid": "https://r2.ropensci.org/0aa03d7d48dce336d6842fdce8dc38b3cb4658e45377bb65ce12c0bfad6f1cf5", "_filesize": 5364820, "_sha256": "0aa03d7d48dce336d6842fdce8dc38b3cb4658e45377bb65ce12c0bfad6f1cf5", "_expires": "2027-01-29T09:20:18.000Z", "_created": "2026-10-21T09:17:07.000Z", "_published": "2026-10-21T09:20:21.134Z", "_buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 119140961358, "time": 131, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12334043613" }, { "job": 119140961447, "time": 134, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12333668813" }, { "job": 119140961388, "time": 136, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "12333084742" }, { "job": 119140961359, "time": 137, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12334601492" }, { "job": 119139641885, "time": 182, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12333363857" }, { "job": 119140961320, "time": 106, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12333139498" }, { "job": 119140961441, "time": 105, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12333527949" }, { "job": 119140961321, "time": 82, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12333403131" }, { "job": 119140961454, "time": 109, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12333868207" } ], "_upstream": "https://github.com/tidymodels/modeldata", "_commit": { "id": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "author": "Max Kuhn ", "committer": "Max Kuhn ", "message": "Increment version number to 1.6.0.9000\n", "time": 1787413144 }, "_maintainer": { "name": "Max Kuhn", "email": "max@posit.co", "login": "topepo", "description": "", "uuid": 5731043 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1", "role": "Depends" }, { "package": "cli", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "rlang", "version": ">= 1.1.0", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "covr", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "tidymodels", "_selfowned": true, "_usedby": 18, "_updates": [ { "week": "2025-46", "n": 1 }, { "week": "2026-32", "n": 1 }, { "week": "2026-34", "n": 2 } ], "_tags": [ { "name": "v1.6.0", "date": "2026-08-22" } ], "_stars": 25, "_contributors": [ { "user": "topepo", "count": 111, "uuid": 5731043 }, { "user": "juliasilge", "count": 25, "uuid": 12505835 }, { "user": "emilhvitfeldt", "count": 15, "uuid": 14034784 }, { "user": "hfrick", "count": 6, "uuid": 12950918 }, { "user": "davisvaughan", "count": 1, "uuid": 19150088 }, { "user": "jeroenjanssens", "count": 1, "uuid": 1368256 }, { "user": "luisdva", "count": 1, "uuid": 5580938 }, { "user": "mdogucu", "count": 1, "uuid": 23044486 }, { "user": "simonpcouch", "count": 1, "uuid": 35748691 } ], "_userbio": { "uuid": 29100987, "type": "organization", "name": "tidymodels", "followers": 574 }, "_downloads": { "count": 28665, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/modeldata" }, "_devurl": "https://github.com/tidymodels/modeldata", "_pkgdown": "https://modeldata.tidymodels.org", "_searchresults": 2928, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/modeldata.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "LICENSE" ], "_homeurl": "https://github.com/tidymodels/modeldata", "_realowner": "tidymodels", "_cranurl": true, "_releases": [ { "version": "0.0.1", "date": "2019-12-06" }, { "version": "0.0.2", "date": "2020-06-22" }, { "version": "0.1.0", "date": "2020-10-22" }, { "version": "0.1.1", "date": "2021-07-14" }, { "version": "1.0.0", "date": "2022-07-01" }, { "version": "1.0.1", "date": "2022-09-06" }, { "version": "1.1.0", "date": "2023-01-25" }, { "version": "1.2.0", "date": "2023-08-09" }, { "version": "1.3.0", "date": "2024-01-21" }, { "version": "1.4.0", "date": "2024-06-19" }, { "version": "1.5.0", "date": "2025-08-01" }, { "version": "1.5.1", "date": "2025-08-22" }, { "version": "1.6.0", "date": "2026-08-21" } ], "_exports": [ "sim_classification", "sim_logistic", "sim_multinomial", "sim_noise", "sim_regression" ], "_datasets": [ { "name": "ad_data", "title": "Alzheimer's disease data", "object": "ad_data", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "ACE_CD143_Angiotensin_Converti", "ACTH_Adrenocorticotropic_Hormon", "AXL", "Adiponectin", "Alpha_1_Antichymotrypsin", "Alpha_1_Antitrypsin", "Alpha_1_Microglobulin", "Alpha_2_Macroglobulin", "Angiopoietin_2_ANG_2", "Angiotensinogen", "Apolipoprotein_A_IV", "Apolipoprotein_A1", "Apolipoprotein_A2", "Apolipoprotein_B", "Apolipoprotein_CI", "Apolipoprotein_CIII", "Apolipoprotein_D", "Apolipoprotein_E", "Apolipoprotein_H", "B_Lymphocyte_Chemoattractant_BL", "BMP_6", "Beta_2_Microglobulin", "Betacellulin", "C_Reactive_Protein", "CD40", "CD5L", "Calbindin", "Calcitonin", "CgA", "Clusterin_Apo_J", "Complement_3", "Complement_Factor_H", "Connective_Tissue_Growth_Factor", "Cortisol", "Creatine_Kinase_MB", "Cystatin_C", "EGF_R", "EN_RAGE", "ENA_78", "Eotaxin_3", "FAS", "FSH_Follicle_Stimulation_Hormon", "Fas_Ligand", "Fatty_Acid_Binding_Protein", "Ferritin", "Fetuin_A", "Fibrinogen", "GRO_alpha", "Gamma_Interferon_induced_Monokin", "Glutathione_S_Transferase_alpha", "HB_EGF", "HCC_4", "Hepatocyte_Growth_Factor_HGF", "I_309", "ICAM_1", "IGF_BP_2", "IL_11", "IL_13", "IL_16", "IL_17E", "IL_1alpha", "IL_3", "IL_4", "IL_5", "IL_6", "IL_6_Receptor", "IL_7", "IL_8", "IP_10_Inducible_Protein_10", "IgA", "Insulin", "Kidney_Injury_Molecule_1_KIM_1", "LOX_1", "Leptin", "Lipoprotein_a", "MCP_1", "MCP_2", "MIF", "MIP_1alpha", "MIP_1beta", "MMP_2", "MMP_3", "MMP10", "MMP7", "Myoglobin", "NT_proBNP", "NrCAM", "Osteopontin", "PAI_1", "PAPP_A", "PLGF", "PYY", "Pancreatic_polypeptide", "Prolactin", "Prostatic_Acid_Phosphatase", "Protein_S", "Pulmonary_and_Activation_Regulat", "RANTES", "Resistin", "S100b", "SGOT", "SHBG", "SOD", "Serum_Amyloid_P", "Sortilin", "Stem_Cell_Factor", "TGF_alpha", "TIMP_1", "TNF_RII", "TRAIL_R3", "TTR_prealbumin", "Tamm_Horsfall_Protein_THP", "Thrombomodulin", "Thrombopoietin", "Thymus_Expressed_Chemokine_TECK", "Thyroid_Stimulating_Hormone", "Thyroxine_Binding_Globulin", "Tissue_Factor", "Transferrin", "Trefoil_Factor_3_TFF3", "VCAM_1", "VEGF", "Vitronectin", "von_Willebrand_Factor", "age", "tau", "p_tau", "Ab_42", "male", "Genotype", "Class" ], "rows": 333, "table": true, "tojson": true }, { "name": "ames", "title": "Ames Housing Data", "object": "ames", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "MS_SubClass", "MS_Zoning", "Lot_Frontage", "Lot_Area", "Street", "Alley", "Lot_Shape", "Land_Contour", "Utilities", "Lot_Config", "Land_Slope", "Neighborhood", "Condition_1", "Condition_2", "Bldg_Type", "House_Style", "Overall_Cond", "Year_Built", "Year_Remod_Add", "Roof_Style", "Roof_Matl", "Exterior_1st", "Exterior_2nd", "Mas_Vnr_Type", "Mas_Vnr_Area", "Exter_Cond", "Foundation", "Bsmt_Cond", "Bsmt_Exposure", "BsmtFin_Type_1", "BsmtFin_SF_1", "BsmtFin_Type_2", "BsmtFin_SF_2", "Bsmt_Unf_SF", "Total_Bsmt_SF", "Heating", "Heating_QC", "Central_Air", "Electrical", "First_Flr_SF", "Second_Flr_SF", "Gr_Liv_Area", "Bsmt_Full_Bath", "Bsmt_Half_Bath", "Full_Bath", "Half_Bath", "Bedroom_AbvGr", "Kitchen_AbvGr", "TotRms_AbvGrd", "Functional", "Fireplaces", "Garage_Type", "Garage_Finish", "Garage_Cars", "Garage_Area", "Garage_Cond", "Paved_Drive", "Wood_Deck_SF", "Open_Porch_SF", "Enclosed_Porch", "Three_season_porch", "Screen_Porch", "Pool_Area", "Pool_QC", "Fence", "Misc_Feature", "Misc_Val", "Mo_Sold", "Year_Sold", "Sale_Type", "Sale_Condition", "Sale_Price", "Longitude", "Latitude" ], "rows": 2930, "table": true, "tojson": true }, { "name": "attrition", "title": "Job attrition", "object": "attrition", "class": [ "data.frame" ], "fields": [ "Age", "Attrition", "BusinessTravel", "DailyRate", "Department", "DistanceFromHome", "Education", "EducationField", "EnvironmentSatisfaction", "Gender", "HourlyRate", "JobInvolvement", "JobLevel", "JobRole", "JobSatisfaction", "MaritalStatus", "MonthlyIncome", "MonthlyRate", "NumCompaniesWorked", "OverTime", "PercentSalaryHike", "PerformanceRating", "RelationshipSatisfaction", "StockOptionLevel", "TotalWorkingYears", "TrainingTimesLastYear", "WorkLifeBalance", "YearsAtCompany", "YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager" ], "rows": 1470, "table": true, "tojson": true }, { "name": "biomass", "title": "Biomass data", "object": "biomass", "class": [ "data.frame" ], "fields": [ "sample", "dataset", "carbon", "hydrogen", "oxygen", "nitrogen", "sulfur", "HHV" ], "rows": 536, "table": true, "tojson": true }, { "name": "bivariate_test", "title": "Example bivariate classification data", "object": "bivariate", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "A", "B", "Class" ], "rows": 710, "table": true, "tojson": true }, { "name": "bivariate_train", "title": "Example bivariate classification data", "object": "bivariate", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "A", "B", "Class" ], "rows": 1009, "table": true, "tojson": true }, { "name": "bivariate_val", "title": "Example bivariate classification data", "object": "bivariate", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "A", "B", "Class" ], "rows": 300, "table": true, "tojson": true }, { "name": "car_prices", "title": "Kelly Blue Book resale data for 2005 model year GM cars", "object": "car_prices", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "Price", "Mileage", "Cylinder", "Doors", "Cruise", "Sound", "Leather", "Buick", "Cadillac", "Chevy", "Pontiac", "Saab", "Saturn", "convertible", "coupe", "hatchback", "sedan", "wagon" ], "rows": 804, "table": true, "tojson": true }, { "name": "cat_adoption", "title": "Cat Adoption", "object": "cat_adoption", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "time", "event", "sex", "neutered", "intake_condition", "intake_type", "latitude", "longitude", "black", "brown", "brown_tabby", "calico", "cream", "gray", "gray_tabby", "orange", "orange_tabby", "tan", "tortie", "white" ], "rows": 2257, "table": true, "tojson": true }, { "name": "cells", "title": "Cell body segmentation", "object": "cells", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "case", "class", "angle_ch_1", "area_ch_1", "avg_inten_ch_1", "avg_inten_ch_2", "avg_inten_ch_3", "avg_inten_ch_4", "convex_hull_area_ratio_ch_1", "convex_hull_perim_ratio_ch_1", "diff_inten_density_ch_1", "diff_inten_density_ch_3", "diff_inten_density_ch_4", "entropy_inten_ch_1", "entropy_inten_ch_3", "entropy_inten_ch_4", "eq_circ_diam_ch_1", "eq_ellipse_lwr_ch_1", "eq_ellipse_oblate_vol_ch_1", "eq_ellipse_prolate_vol_ch_1", "eq_sphere_area_ch_1", "eq_sphere_vol_ch_1", "fiber_align_2_ch_3", "fiber_align_2_ch_4", "fiber_length_ch_1", "fiber_width_ch_1", "inten_cooc_asm_ch_3", "inten_cooc_asm_ch_4", "inten_cooc_contrast_ch_3", "inten_cooc_contrast_ch_4", "inten_cooc_entropy_ch_3", "inten_cooc_entropy_ch_4", "inten_cooc_max_ch_3", "inten_cooc_max_ch_4", "kurt_inten_ch_1", "kurt_inten_ch_3", "kurt_inten_ch_4", "length_ch_1", "neighbor_avg_dist_ch_1", "neighbor_min_dist_ch_1", "neighbor_var_dist_ch_1", "perim_ch_1", "shape_bfr_ch_1", "shape_lwr_ch_1", "shape_p_2_a_ch_1", "skew_inten_ch_1", "skew_inten_ch_3", "skew_inten_ch_4", "spot_fiber_count_ch_3", "spot_fiber_count_ch_4", "total_inten_ch_1", "total_inten_ch_2", "total_inten_ch_3", "total_inten_ch_4", "var_inten_ch_1", "var_inten_ch_3", "var_inten_ch_4", "width_ch_1" ], "rows": 2019, "table": true, "tojson": true }, { "name": "check_times", "title": "Execution time data", "object": "check_times", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "package", "authors", "imports", "suggests", "depends", "Roxygen", "gh", "rforge", "descr", "r_count", "r_size", "ns_import", "ns_export", "s3_methods", "s4_methods", "doc_count", "doc_size", "src_count", "src_size", "data_count", "data_size", "testthat_count", "testthat_size", "check_time", "status" ], "rows": 13626, "table": true, "tojson": true }, { "name": "chem_proc_yield", "title": "Chemical manufacturing process data set", "object": "chem_proc_yield", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "yield", "bio_material_01", "bio_material_02", "bio_material_03", "bio_material_04", "bio_material_05", "bio_material_06", "bio_material_07", "bio_material_08", "bio_material_09", "bio_material_10", "bio_material_11", "bio_material_12", "man_proc_01", "man_proc_02", "man_proc_03", "man_proc_04", "man_proc_05", "man_proc_06", "man_proc_07", "man_proc_08", "man_proc_09", "man_proc_10", "man_proc_11", "man_proc_12", "man_proc_13", "man_proc_14", "man_proc_15", "man_proc_16", "man_proc_17", "man_proc_18", "man_proc_19", "man_proc_20", "man_proc_21", "man_proc_22", "man_proc_23", "man_proc_24", "man_proc_25", "man_proc_26", "man_proc_27", "man_proc_28", "man_proc_29", "man_proc_30", "man_proc_31", "man_proc_32", "man_proc_33", "man_proc_34", "man_proc_35", "man_proc_36", "man_proc_37", "man_proc_38", "man_proc_39", "man_proc_40", "man_proc_41", "man_proc_42", "man_proc_43", "man_proc_44", "man_proc_45" ], "rows": 176, "table": true, "tojson": true }, { "name": "Chicago", "title": "Chicago ridership data", "object": "Chicago", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "ridership", "Austin", "Quincy_Wells", "Belmont", "Archer_35th", "Oak_Park", "Western", "Clark_Lake", "Clinton", "Merchandise_Mart", "Irving_Park", "Washington_Wells", "Harlem", "Monroe", "Polk", "Ashland", "Kedzie", "Addison", "Jefferson_Park", "Montrose", "California", "temp_min", "temp", "temp_max", "temp_change", "dew", "humidity", "pressure", "pressure_change", "wind", "wind_max", "gust", "gust_max", "percip", "percip_max", "weather_rain", "weather_snow", "weather_cloud", "weather_storm", "Blackhawks_Away", "Blackhawks_Home", "Bulls_Away", "Bulls_Home", "Bears_Away", "Bears_Home", "WhiteSox_Away", "WhiteSox_Home", "Cubs_Away", "Cubs_Home", "date" ], "rows": 5698, "table": true, "tojson": true }, { "name": "cls_data_2026", "title": "Two classes, Two predictors 2026 Edition", "object": "cls_data_2026", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "pred_1", "pred_2", "class" ], "rows": 1000, "table": true, "tojson": true }, { "name": "concrete", "title": "Compressive strength of concrete mixtures", "object": "concrete", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "cement", "blast_furnace_slag", "fly_ash", "water", "superplasticizer", "coarse_aggregate", "fine_aggregate", "age", "compressive_strength" ], "rows": 1030, "table": true, "tojson": true }, { "name": "covers", "title": "Raw cover type data", "object": "covers", "class": [ "data.frame" ], "fields": [ "description" ], "rows": 40, "table": true, "tojson": true }, { "name": "credit_data", "title": "Credit data", "object": "credit_data", "class": [ "data.frame" ], "fields": [ "Status", "Seniority", "Home", "Time", "Age", "Marital", "Records", "Job", "Expenses", "Income", "Assets", "Debt", "Amount", "Price" ], "rows": 4454, "table": true, "tojson": true }, { "name": "crickets", "title": "Rates of Cricket Chirps", "object": "crickets", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "species", "temp", "rate" ], "rows": 31, "table": true, "tojson": true }, { "name": "deliveries", "title": "Food Delivery Time Data", "object": "deliveries", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "time_to_delivery", "hour", "day", "distance", "item_01", "item_02", "item_03", "item_04", "item_05", "item_06", "item_07", "item_08", "item_09", "item_10", "item_11", "item_12", "item_13", "item_14", "item_15", "item_16", "item_17", "item_18", "item_19", "item_20", "item_21", "item_22", "item_23", "item_24", "item_25", "item_26", "item_27" ], "rows": 10012, "table": true, "tojson": true }, { "name": "drinks", "title": "Sample time series data", "object": "drinks", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "date", "S4248SM144NCEN" ], "rows": 309, "table": true, "tojson": true }, { "name": "grants_2008", "title": "Grant acceptance data", "object": "grants", "class": [ "integer" ], "fields": [], "table": false, "tojson": true }, { "name": "grants_other", "title": "Grant acceptance data", "object": "grants", "class": [ "data.frame" ], "fields": [], "rows": 8190, "table": true, "tojson": true }, { "name": "grants_test", "title": "Grant acceptance data", "object": "grants", "class": [ "data.frame" ], "fields": [], "rows": 518, "table": true, "tojson": true }, { "name": "hepatic_injury_qsar", "title": "Predicting hepatic injury from chemical information", "object": "hepatic_injury_qsar", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [], "rows": 281, "table": true, "tojson": true }, { "name": "hotel_rates", "title": "Daily Hotel Rate Data", "object": "hotel_rates", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "avg_price_per_room", "lead_time", "stays_in_weekend_nights", "stays_in_week_nights", "adults", "children", "babies", "meal", "country", "market_segment", "distribution_channel", "is_repeated_guest", "previous_cancellations", "previous_bookings_not_canceled", "reserved_room_type", "assigned_room_type", "booking_changes", "agent", "company", "days_in_waiting_list", "customer_type", "required_car_parking_spaces", "total_of_special_requests", "arrival_date", "arrival_date_num", "near_christmas", "near_new_years", "historical_adr" ], "rows": 15402, "table": true, "tojson": true }, { "name": "hpc_cv", "title": "Class probability predictions", "object": "hpc_cv", "class": [ "data.frame" ], "fields": [ "obs", "pred", "VF", "F", "M", "L", "Resample" ], "rows": 3467, "table": true, "tojson": true }, { "name": "hpc_data", "title": "High-performance computing system data", "object": "hpc_data", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "protocol", "compounds", "input_fields", "iterations", "num_pending", "hour", "day", "class" ], "rows": 4331, "table": true, "tojson": true }, { "name": "ischemic_stroke", "title": "Clinical data used to predict ischemic stroke", "object": "ischemic_stroke", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "stroke", "nascet_scale", "calc_vol", "calc_vol_prop", "matx_vol", "matx_vol_prop", "lrnc_vol", "lrnc_vol_prop", "max_calc_area", "max_calc_area_prop", "max_dilation_by_area", "max_matx_area", "max_matx_area_prop", "max_lrnc_area", "max_lrnc_area_prop", "max_max_wall_thickness", "max_remodeling_ratio", "max_stenosis_by_area", "max_wall_area", "wall_vol", "max_stenosis_by_diameter", "age", "male", "smoking_history", "atrial_fibrillation", "coronary_artery_disease", "diabetes_history", "hypercholesterolemia_history", "hypertension_history" ], "rows": 126, "table": true, "tojson": true }, { "name": "leaf_id_flavia", "title": "Leaf identification data (Flavia)", "object": "leaf_id_flavia", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "species", "apex", "base", "shape", "denate_edge", "lobed_edge", "smooth_edge", "toothed_edge", "undulate_edge", "outlying_polar", "skewed_polar", "clumpy_polar", "sparse_polar", "striated_polar", "convex_polar", "skinny_polar", "stringy_polar", "monotonic_polar", "outlying_contour", "skewed_contour", "clumpy_contour", "sparse_contour", "striated_contour", "convex_contour", "skinny_contour", "stringy_contour", "monotonic_contour", "num_max_points", "num_min_points", "diameter", "area", "perimeter", "physiological_length", "physiological_width", "aspect_ratio", "rectangularity", "circularity", "compactness", "narrow_factor", "perimeter_ratio_diameter", "perimeter_ratio_length", "perimeter_ratio_lw", "num_convex_points", "perimeter_convexity", "area_convexity", "area_ratio_convexity", "equivalent_diameter", "eccentricity", "contrast", "correlation_texture", "inverse_difference_moments", "entropy", "mean_red_val", "mean_green_val", "mean_blue_val", "std_red_val", "std_green_val", "std_blue_val", "correlation" ], "rows": 1907, "table": true, "tojson": true }, { "name": "lending_club", "title": "Loan data", "object": "lending_club", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "funded_amnt", "term", "int_rate", "sub_grade", "addr_state", "verification_status", "annual_inc", "emp_length", "delinq_2yrs", "inq_last_6mths", "revol_util", "acc_now_delinq", "open_il_6m", "open_il_12m", "open_il_24m", "total_bal_il", "all_util", "inq_fi", "inq_last_12m", "delinq_amnt", "num_il_tl", "total_il_high_credit_limit", "Class" ], "rows": 9857, "table": true, "tojson": true }, { "name": "meats", "title": "Fat, water and protein content of meat samples", "object": "meats", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "x_001", "x_002", "x_003", "x_004", "x_005", "x_006", "x_007", "x_008", "x_009", "x_010", "x_011", "x_012", "x_013", "x_014", "x_015", "x_016", "x_017", "x_018", "x_019", "x_020", "x_021", "x_022", "x_023", "x_024", "x_025", "x_026", "x_027", "x_028", "x_029", "x_030", "x_031", "x_032", "x_033", "x_034", "x_035", "x_036", "x_037", "x_038", "x_039", "x_040", "x_041", "x_042", "x_043", "x_044", "x_045", "x_046", "x_047", "x_048", "x_049", "x_050", "x_051", "x_052", "x_053", "x_054", "x_055", "x_056", "x_057", "x_058", "x_059", "x_060", "x_061", "x_062", "x_063", "x_064", "x_065", "x_066", "x_067", "x_068", "x_069", "x_070", "x_071", "x_072", "x_073", "x_074", "x_075", "x_076", "x_077", "x_078", "x_079", "x_080", "x_081", "x_082", "x_083", "x_084", "x_085", "x_086", "x_087", "x_088", "x_089", "x_090", "x_091", "x_092", "x_093", "x_094", "x_095", "x_096", "x_097", "x_098", "x_099", "x_100", "water", "fat", "protein" ], "rows": 215, "table": true, "tojson": true }, { "name": "mlc_churn", "title": "Customer churn data", "object": "mlc_churn", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "state", "account_length", "area_code", "international_plan", "voice_mail_plan", "number_vmail_messages", "total_day_minutes", "total_day_calls", "total_day_charge", "total_eve_minutes", "total_eve_calls", "total_eve_charge", "total_night_minutes", "total_night_calls", "total_night_charge", "total_intl_minutes", "total_intl_calls", "total_intl_charge", "number_customer_service_calls", "churn" ], "rows": 5000, "table": true, "tojson": true }, { "name": "oils", "title": "Fatty acid composition of commercial oils", "object": "oils", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "palmitic", "stearic", "oleic", "linoleic", "linolenic", "eicosanoic", "eicosenoic", "class" ], "rows": 96, "table": true, "tojson": true }, { "name": "parabolic", "title": "Parabolic class boundary data", "object": "parabolic", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "X1", "X2", "class" ], "rows": 500, "table": true, "tojson": true }, { "name": "pathology", "title": "Liver pathology data", "object": "pathology", "class": [ "data.frame" ], "fields": [ "pathology", "scan" ], "rows": 344, "table": true, "tojson": true }, { "name": "pd_speech", "title": "Parkinson's disease speech classification data set", "object": "pd_speech", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [], "rows": 252, "table": true, "tojson": true }, { "name": "penguins", "title": "Palmer Station penguin data", "object": "penguins", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "species", "island", "bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", "sex" ], "rows": 344, "table": true, "tojson": true }, { "name": "permeability_qsar", "title": "Predicting permeability from chemical information", "object": "permeability_qsar", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [], "rows": 165, "table": true, "tojson": true }, { "name": "Sacramento", "title": "Sacramento CA home prices", "object": "Sacramento", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "city", "zip", "beds", "baths", "sqft", "type", "price", "latitude", "longitude" ], "rows": 932, "table": true, "tojson": true }, { "name": "scat", "title": "Morphometric data on scat", "object": "scat", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "Species", "Month", "Year", "Site", "Location", "Age", "Number", "Length", "Diameter", "Taper", "TI", "Mass", "d13C", "d15N", "CN", "ropey", "segmented", "flat", "scrape" ], "rows": 110, "table": true, "tojson": true }, { "name": "Smithsonian", "title": "Smithsonian museums", "object": "Smithsonian", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "name", "latitude", "longitude" ], "rows": 20, "table": true, "tojson": true }, { "name": "solubility_test", "title": "Solubility predictions from MARS model", "object": "solubility_test", "class": [ "data.frame" ], "fields": [ "solubility", "prediction" ], "rows": 316, "table": true, "tojson": true }, { "name": "stackoverflow", "title": "Annual Stack Overflow Developer Survey Data", "object": "stackoverflow", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "Country", "Salary", "YearsCodedJob", "OpenSource", "Hobby", "CompanySizeNumber", "Remote", "CareerSatisfaction", "Data_scientist", "Database_administrator", "Desktop_applications_developer", "Developer_with_stats_math_background", "DevOps", "Embedded_developer", "Graphic_designer", "Graphics_programming", "Machine_learning_specialist", "Mobile_developer", "Quality_assurance_engineer", "Systems_administrator", "Web_developer" ], "rows": 5594, "table": true, "tojson": true }, { "name": "stations", "title": "Chicago ridership data", "object": "Chicago", "class": [ "character" ], "fields": [], "table": false, "tojson": true }, { "name": "steroidogenic_toxicity", "title": "Predicting steroidogenic toxicity with assay data", "object": "steroidogenic_toxicity", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "class", "cyp_11a1", "cyp_11b1", "cyp_11b2", "cyp_17a1", "cyp_19a1", "cyp_21a1", "hsd3b2", "star", "progesterone", "testosterone", "dhea", "cortisol" ], "rows": 162, "table": true, "tojson": true }, { "name": "tate_text", "title": "Tate Gallery modern artwork metadata", "object": "tate_text", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "id", "artist", "title", "medium", "year" ], "rows": 4284, "table": true, "tojson": true }, { "name": "taxi", "title": "Chicago taxi data set", "object": "taxi", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "tip", "distance", "company", "local", "dow", "month", "hour" ], "rows": 10000, "table": true, "tojson": true }, { "name": "testing_data", "title": "Fine foods example data", "object": "small_fine_foods", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "product", "review", "score" ], "rows": 1000, "table": true, "tojson": true }, { "name": "training_data", "title": "Fine foods example data", "object": "small_fine_foods", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "product", "review", "score" ], "rows": 4000, "table": true, "tojson": true }, { "name": "two_class_dat", "title": "Two class data", "object": "two_class_dat", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "A", "B", "Class" ], "rows": 791, "table": true, "tojson": true }, { "name": "two_class_example", "title": "Two class predictions", "object": "two_class_example", "class": [ "data.frame" ], "fields": [ "truth", "Class1", "Class2", "predicted" ], "rows": 500, "table": true, "tojson": true }, { "name": "wa_churn", "title": "Watson churn data", "object": "wa_churn", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "churn", "female", "senior_citizen", "partner", "dependents", "tenure", "phone_service", "multiple_lines", "internet_service", "online_security", "online_backup", "device_protection", "tech_support", "streaming_tv", "streaming_movies", "contract", "paperless_billing", "payment_method", "monthly_charges", "total_charges" ], "rows": 7043, "table": true, "tojson": true } ], "_help": [ { "page": "ad_data", "title": "Alzheimer's disease data", "topics": [ "ad_data" ] }, { "page": "ames", "title": "Ames Housing Data", "topics": [ "ames" ] }, { "page": "attrition", "title": "Job attrition", "topics": [ "attrition" ] }, { "page": "biomass", "title": "Biomass data", "topics": [ "biomass" ] }, { "page": "bivariate", "title": "Example bivariate classification data", "topics": [ "bivariate", "bivariate_test", "bivariate_train", "bivariate_val" ] }, { "page": "car_prices", "title": "Kelly Blue Book resale data for 2005 model year GM cars", "topics": [ "car_prices" ] }, { "page": "cat_adoption", "title": "Cat Adoption", "topics": [ "cat_adoption" ] }, { "page": "cells", "title": "Cell body segmentation", "topics": [ "cells" ] }, { "page": "check_times", "title": "Execution time data", "topics": [ "check_times" ] }, { "page": "chem_proc_yield", "title": "Chemical manufacturing process data set", "topics": [ "chem_proc_yield" ] }, { "page": "Chicago", "title": "Chicago ridership data", "topics": [ "Chicago", "stations" ] }, { "page": "cls_data_2026", "title": "Two classes, Two predictors 2026 Edition", "topics": [ "cls_data_2026" ] }, { "page": "concrete", "title": "Compressive strength of concrete mixtures", "topics": [ "concrete" ] }, { "page": "covers", "title": "Raw cover type data", "topics": [ "covers" ] }, { "page": "credit_data", "title": "Credit data", "topics": [ "credit_data" ] }, { "page": "crickets", "title": "Rates of Cricket Chirps", "topics": [ "crickets" ] }, { "page": "deliveries", "title": "Food Delivery Time Data", "topics": [ "deliveries" ] }, { "page": "drinks", "title": "Sample time series data", "topics": [ "drinks" ] }, { "page": "grants", "title": "Grant acceptance data", "topics": [ "grants", "grants_2008", "grants_other", "grants_test" ] }, { "page": "hepatic_injury_qsar", "title": "Predicting hepatic injury from chemical information", "topics": [ "hepatic_injury_qsar" ] }, { "page": "hotel_rates", "title": "Daily Hotel Rate Data", "topics": [ "hotel_rates" ] }, { "page": "hpc_cv", "title": "Class probability predictions", "topics": [ "hpc_cv" ] }, { "page": "hpc_data", "title": "High-performance computing system data", "topics": [ "hpc_data" ] }, { "page": "ischemic_stroke", "title": "Clinical data used to predict ischemic stroke", "topics": [ "ischemic_stroke" ] }, { "page": "leaf_id_flavia", "title": "Leaf identification data (Flavia)", "topics": [ "leaf_id_flavia" ] }, { "page": "lending_club", "title": "Loan data", "topics": [ "lending_club" ] }, { "page": "meats", "title": "Fat, water and protein content of meat samples", "topics": [ "meats" ] }, { "page": "mlc_churn", "title": "Customer churn data", "topics": [ "mlc_churn" ] }, { "page": "oils", "title": "Fatty acid composition of commercial oils", "topics": [ "oils" ] }, { "page": "parabolic", "title": "Parabolic class boundary data", "topics": [ "parabolic" ] }, { "page": "pathology", "title": "Liver pathology data", "topics": [ "pathology" ] }, { "page": "pd_speech", "title": "Parkinson's disease speech classification data set", "topics": [ "pd_speech" ] }, { "page": "penguins", "title": "Palmer Station penguin data", "topics": [ "penguins" ] }, { "page": "permeability_qsar", "title": "Predicting permeability from chemical information", "topics": [ "permeability_qsar" ] }, { "page": "Sacramento", "title": "Sacramento CA home prices", "topics": [ "Sacramento" ] }, { "page": "scat", "title": "Morphometric data on scat", "topics": [ "scat" ] }, { "page": "sim_classification", "title": "Simulate datasets", "topics": [ "sim_classification", "sim_logistic", "sim_multinomial", "sim_noise", "sim_regression" ] }, { "page": "small_fine_foods", "title": "Fine foods example data", "topics": [ "small_fine_foods", "testing_data", "training_data" ] }, { "page": "Smithsonian", "title": "Smithsonian museums", "topics": [ "Smithsonian" ] }, { "page": "solubility_test", "title": "Solubility predictions from MARS model", "topics": [ "solubility_test" ] }, { "page": "stackoverflow", "title": "Annual Stack Overflow Developer Survey Data", "topics": [ "stackoverflow" ] }, { "page": "steroidogenic_toxicity", "title": "Predicting steroidogenic toxicity with assay data", "topics": [ "steroidogenic_toxicity" ] }, { "page": "tate_text", "title": "Tate Gallery modern artwork metadata", "topics": [ "tate_text" ] }, { "page": "taxi", "title": "Chicago taxi data set", "topics": [ "taxi" ] }, { "page": "two_class_dat", "title": "Two class data", "topics": [ "two_class_dat" ] }, { "page": "two_class_example", "title": "Two class predictions", "topics": [ "two_class_example" ] }, { "page": "wa_churn", "title": "Watson churn data", "topics": [ "wa_churn" ] } ], "_readme": "https://github.com/tidymodels/modeldata/raw/HEAD/README.md", "_rundeps": [ "cli", "dplyr", "generics", "glue", "lifecycle", "magrittr", "MASS", "pillar", "pkgconfig", "purrr", "R6", "rlang", "tibble", "tidyselect", "utf8", "vctrs", "withr" ], "_score": 11.133438033039681, "_indexed": true, "_nocasepkg": "modeldata", "_universes": [ "tidymodels", "topepo" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.6.0.9000", "date": "2026-10-21T09:19:15.000Z", "distro": "resolute", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/01de6f18dff898a5fbd19464c6a998108ca45339756c908d7b974b579833a2de", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.6.1", "os": "linux", "version": "1.6.0.9000", "date": "2026-10-21T09:19:14.000Z", "distro": "resolute", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/6094d58edd17aef788bc36a4ac0f92984994a4d0879554c1430c6ea2c3f7a89d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.5.3", "os": "mac", "version": "1.6.0.9000", "date": "2026-10-21T09:19:21.000Z", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/469fd10239f451ba83319d1693296c9906710cecadbf2bb1cf09ede14cd4064f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.6.1", "os": "mac", "version": "1.6.0.9000", "date": "2026-10-21T09:19:23.000Z", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/6cd2352bda8f79fd58c4b129a9a4b8ca2ef84600137f6b0f18e695590a60c5f1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.6.0", "os": "wasm", "version": "1.6.0.9000", "date": "2026-10-21T09:19:16.000Z", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/77772338c7a0718a9646c99dc4db254e3f358e23e1237e7013d2ec9feeb1defd", "status": "success", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.7.0", "os": "win", "version": "1.6.0.9000", "date": "2026-10-21T09:18:32.000Z", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/e6b982bd544c16293b0da92b310813446549d34865b7992fd3834a055e852d0d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.5.3", "os": "win", "version": "1.6.0.9000", "date": "2026-10-21T09:18:22.000Z", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/2155fcea1150e99645190844e49bde30deda2adb5b545da33e5d5acd56db4429", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" }, { "r": "4.6.1", "os": "win", "version": "1.6.0.9000", "date": "2026-10-21T09:18:50.000Z", "commit": "00f1e0a7d1a08d38da02c09133f7f90b2efae318", "fileid": "https://r2.ropensci.org/98485c23b0cbd8f70b3d35c184a6219c5c916f1511bdb38e37162809089081ef", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/tidymodels/actions/runs/39575387877" } ] }