{ "_id": "6a43c4af58db26aa3c3594a5", "Package": "UKBAnalytica", "Title": "UK Biobank Data Processing and Survival Analysis Toolkit", "Version": "1.0.0", "Authors@R": "person(given = \"Nan\", family = \"He\", email = \"hinna01@163.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0008-6932-3867\"))", "Author": "Nan He [aut, cre] (ORCID:\n)", "Maintainer": "Nan He ", "Description": "Provides an integrated workflow for UK Biobank Research\nAnalysis Platform (RAP) hosted and RAP-generated analysis\ntables. The package supports RAP phenotype extraction planning,\npredefined variable sets and disease definitions, standardized\nbaseline preprocessing, multi-source endpoint ascertainment,\nprevalent and incident case classification, survival-ready\ncohort construction, regression, multiple imputation,\npropensity score analysis, mediation analysis, subgroup and\nsensitivity analyses, machine learning, proteomics enrichment\nand protein-protein interaction analysis, and\npublication-oriented visualization. The package workflow is\ndescribed in He et al. (2026)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "URL": "https://github.com/Hinna0818/UKBAnalytica,\nhttps://hinna0818.github.io/UKBAnalytica/", "BugReports": "https://github.com/Hinna0818/UKBAnalytica/issues", "Config/testthat/edition": "3", "Config/Needs/bioc": "TRUE", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-30 13:23:34 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-30 11:57:10 UTC", "RemoteUrl": "https://github.com/cran/UKBAnalytica", "RemoteRef": "HEAD", "RemoteSha": "38137fe91b0c96d2b354e05d431568df3416cab3", "MD5sum": "6136b567c8d741f4354fd3d7d7d1c022", "_user": "cran", "_type": "src", "_file": "UKBAnalytica_1.0.0.tar.gz", "_fileid": "78bbd66f50e84b54910b97a69a8edad3c19be0f473b3542ee8fd2333bf29fb1c", "_filesize": 1489457, "_sha256": "78bbd66f50e84b54910b97a69a8edad3c19be0f473b3542ee8fd2333bf29fb1c", "_created": "2026-06-30T13:23:34.000Z", "_published": "2026-06-30T13:29:19.289Z", "_distro": "resolute", "_jobs": [ { "job": 84301367429, "time": 291, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7982014692" }, { "job": 84301367415, "time": 284, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "7982011536" }, { "job": 84300126634, "time": 335, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "7981884643" }, { "job": 84301367359, "time": 252, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7981996780" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/28447327359", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/UKBAnalytica", "_commit": { "id": "38137fe91b0c96d2b354e05d431568df3416cab3", "author": "Nan He ", "committer": "cran-robot ", "message": "version 1.0.0\n", "time": 1782820630 }, "_maintainer": { "name": "Nan He", "email": "hinna01@163.com", "login": "mysteriouslaolong", "description": "", "uuid": 270675323 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0", "role": "Depends" }, { "package": "data.table", "role": "Imports" }, { "package": "stringi", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "survival", "role": "Imports" }, { "package": "pROC", "role": "Imports" }, { "package": "tableone", "role": "Imports" }, { "package": "mice", "role": "Imports" }, { "package": "mitools", "role": "Imports" }, { "package": "sandwich", "role": "Imports" }, { "package": "lmtest", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "mgcv", "role": "Imports" }, { "package": "xml2", "role": "Imports" }, { "package": "igraph", "role": "Imports" }, { "package": "testthat", "role": "Suggests" }, { "package": "rms", "role": "Suggests" }, { "package": "gbm", "role": "Suggests" }, { "package": "cobalt", "role": "Suggests" }, { "package": "survminer", "role": "Suggests" }, { "package": "MatchIt", "role": "Suggests" }, { "package": "regmedint", "role": "Suggests" }, { "package": "ranger", "role": "Suggests" }, { "package": "xgboost", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "e1071", "role": "Suggests" }, { "package": "nnet", "role": "Suggests" }, { "package": "rpart", "role": "Suggests" }, { "package": "Boruta", "role": "Suggests" }, { "package": "rBayesianOptimization", "role": "Suggests" }, { "package": "randomForestSRC", "role": "Suggests" }, { "package": "clusterProfiler", "role": "Suggests" }, { "package": "AnnotationDbi", "role": "Suggests" }, { "package": "org.Hs.eg.db", "role": "Suggests" }, { "package": "qs2", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-27", "n": 1 } ], "_tags": [ { "name": "1.0.0", "date": "2026-06-30" } ], "_stars": 0, "_contributors": [ { "user": "mysteriouslaolong", "count": 1, "uuid": 270675323 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "followers": 614, "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/UKBAnalytica" }, "_devurl": "https://github.com/hinna0818/ukbanalytica", "_searchresults": 43, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/UKBAnalytica.html", "LICENSE", "manual.pdf" ], "_cranurl": false, "_releases": [ { "version": "1.0.0", "date": "2026-06-30" } ], "_exports": [ "assess_balance", "build_survival_dataset", "calculate_air_pollution", "calculate_blood_pressure", "calculate_diet_score", "calculate_weights", "classify_metabolites", "combine_disease_definitions", "compare_data_sources", "compute_protein_ppi_metrics", "create_baseline_table", "create_disease_definition", "create_imputation_list", "create_medication_definition", "estimate_propensity_score", "extract_cases_by_source", "extract_diabetes_subtype_baseline", "extract_disease_diagnosis", "extract_disease_history", "extract_disease_history_sensitivity", "extract_medications", "extract_self_report_medications", "fit_mi_models", "get_death_dates", "get_disease_catalog", "get_field_info", "get_field_metadata", "get_medication_catalog", "get_pomegranate_diseases", "get_pomegranate_source_manifest", "get_predefined_diseases", "get_predefined_medications", "get_protein_ppi", "get_ukb_demo_colnames", "get_variable_info", "get_variable_set", "get_variable_sets", "load_pomegranate_portal_coding", "load_ukb_medication_coding", "load_ukb_metabolite_panel", "match_propensity", "metabolite_to_metaboanalyst_name", "parse_cancer_registry", "parse_death_records", "parse_icd10_diagnoses", "parse_icd9_diagnoses", "parse_opcs4_procedures", "parse_self_reported_illnesses", "plot_balance", "plot_calibration", "plot_correlation", "plot_cox_loghr_correlation", "plot_cox_sensitivity_correlation", "plot_enrichment_lollipop", "plot_forest", "plot_go_ora_bar", "plot_heatmap", "plot_km_curve", "plot_mediation", "plot_mediation_forest", "plot_metabolite_ora_barplot", "plot_metabolite_ora_dotplot", "plot_mi_diagnostics", "plot_mi_pooled", "plot_ml_calibration", "plot_ml_compare", "plot_ml_confusion", "plot_ml_dca", "plot_ml_gain", "plot_ml_importance", "plot_ml_ks", "plot_ml_lift", "plot_ml_pr", "plot_ml_roc", "plot_ml_roc_compare", "plot_participant_flow", "plot_ps_distribution", "plot_rcs", "plot_regression_volcano", "plot_scatter", "plot_shap_beeswarm", "plot_shap_dependence", "plot_shap_force", "plot_shap_summary", "plot_stacked_bar", "plot_top_hr_bars", "plot_violin", "pool_custom_estimates", "pool_mi_models", "preprocess_baseline", "protein_to_gene_symbol", "rank_protein_ppi_nodes", "rap_extract_pheno", "rap_find_dataset", "rap_list_fields", "rap_plan_extract", "rap_submit_extract", "run_correlation", "run_imputation", "run_mediation", "run_metabolite_ora", "run_multi_mediator", "run_multi_subgroup", "run_protein_kegg_ora", "run_protein_ora", "run_protein_ppi_clustering", "run_protein_ppi_robustness", "run_rcs", "run_regression", "run_sensitivity_mediation", "run_subgroup_analysis", "run_weighted_analysis", "runmulti_competing", "runmulti_cox", "runmulti_cox_lag", "runmulti_cox_zph", "runmulti_gam", "runmulti_glm", "runmulti_lm", "runmulti_logit", "runmulti_negbin", "runmulti_trend", "score_protein_ppi_clusters", "select_incident_by_years", "sensitivity_exclude_early_events", "sensitivity_exclude_missing_covariates", "subset_protein_ppi", "tidy.mi_pooled_result", "ukb_check_rap_env", "ukb_clean_missing", "ukb_compare_cox_results", "ukb_compare_sensitivity_cox", "ukb_cox_diagnostics", "ukb_create_extraction_manifest", "ukb_decode", "ukb_decode_column_names", "ukb_decode_values", "ukb_demo", "ukb_download_rap_dictionary", "ukb_extract_fields", "ukb_field_info", "ukb_metadata_setup", "ukb_ml_as_split", "ukb_ml_calibration", "ukb_ml_compare", "ukb_ml_compare_feature_sets", "ukb_ml_compare_flows", "ukb_ml_confusion", "ukb_ml_cv", "ukb_ml_dca", "ukb_ml_evaluate_test", "ukb_ml_feature_select", "ukb_ml_fit_final", "ukb_ml_flow", "ukb_ml_gain_lift", "ukb_ml_importance", "ukb_ml_ks", "ukb_ml_metrics", "ukb_ml_model", "ukb_ml_pr", "ukb_ml_predict", "ukb_ml_roc", "ukb_ml_roc_data", "ukb_ml_split_data", "ukb_ml_supported_models", "ukb_ml_survival", "ukb_ml_survival_as_split", "ukb_ml_survival_evaluate_test", "ukb_ml_survival_feature_select", "ukb_ml_survival_fit_final", "ukb_ml_survival_importance", "ukb_ml_survival_predict", "ukb_ml_survival_shap", "ukb_ml_survival_split_data", "ukb_ml_survival_tune", "ukb_ml_survival_workflow", "ukb_ml_threshold", "ukb_ml_tune", "ukb_ml_workflow", "ukb_participant_flow", "ukb_protein_annotation", "ukb_query_dictionary", "ukb_scale_with_parameters", "ukb_search_fields", "ukb_sensitivity_suite", "ukb_shap", "ukb_shap_dependence", "ukb_shap_force", "ukb_shap_summary", "ukb_snapshot", "ukb_standardize_by_train", "ukb_time_skeleton", "ukb_top_hr_results", "ukb_train_validation_cox", "ukb_validate_columns", "ukb_write_extraction_manifest" ], "_datasets": [ { "name": "ukb_dictionary_zh", "title": "Chinese UK Biobank field-path dictionary", "object": "ukb_dictionary_zh", "class": [ "data.frame" ], "fields": [ "类别1", "类别2", "类别3", "类别4", "类别5", "类别6" ], "rows": 34953, "table": true, "tojson": true } ], "_help": [ { "page": "assess_balance", "title": "Assess Covariate Balance", "topics": [ "assess_balance" ] }, { "page": "build_survival_dataset", "title": "Build Survival Analysis Dataset", "topics": [ "build_survival_dataset" ] }, { "page": "calculate_air_pollution", "title": "Calculate air pollution exposure averages", "topics": [ "calculate_air_pollution" ] }, { "page": "calculate_blood_pressure", "title": "Calculate blood pressure from multiple readings", "topics": [ "calculate_blood_pressure" ] }, { "page": "calculate_diet_score", "title": "Calculate diet score", "topics": [ "calculate_diet_score" ] }, { "page": "calculate_weights", "title": "Calculate IPTW Weights", "topics": [ "calculate_weights" ] }, { "page": "classify_metabolites", "title": "Classify UK Biobank metabolite names", "topics": [ "classify_metabolites" ] }, { "page": "coef.mediation_result", "title": "Extract Coefficients from Mediation Results", "topics": [ "coef.mediation_result" ] }, { "page": "combine_disease_definitions", "title": "Combine Multiple Disease Definitions", "topics": [ "combine_disease_definitions" ] }, { "page": "compare_data_sources", "title": "Compare Case Counts Across Data Sources", "topics": [ "compare_data_sources" ] }, { "page": "compute_protein_ppi_metrics", "title": "Compute topological metrics for a PPI network", "topics": [ "compute_protein_ppi_metrics" ] }, { "page": "confint.mediation_result", "title": "Confidence Intervals for Mediation Results", "topics": [ "confint.mediation_result" ] }, { "page": "create_baseline_table", "title": "Create a baseline table comparing cases and controls under different conditions.", "topics": [ "create_baseline_table" ] }, { "page": "create_disease_definition", "title": "Create Disease Definition Object", "topics": [ "create_disease_definition" ] }, { "page": "create_imputation_list", "title": "Create an imputationList Object", "topics": [ "create_imputation_list" ] }, { "page": "create_medication_definition", "title": "Create a medication definition object", "topics": [ "create_medication_definition" ] }, { "page": "estimate_propensity_score", "title": "Estimate Propensity Score", "topics": [ "estimate_propensity_score" ] }, { "page": "extract_cases_by_source", "title": "Extract Cases by Specified Data Sources", "topics": [ "extract_cases_by_source" ] }, { "page": "extract_diabetes_subtype_baseline", "title": "Extract Baseline Diabetes Subtypes (T1DM/T2DM) with HbA1c Support", "topics": [ "extract_diabetes_subtype_baseline" ] }, { "page": "extract_disease_diagnosis", "title": "Extract participant-level disease diagnosis status", "topics": [ "extract_disease_diagnosis" ] }, { "page": "extract_disease_history", "title": "Extract Disease History (Prevalent Cases) for Covariates", "topics": [ "extract_disease_history" ] }, { "page": "extract_disease_history_sensitivity", "title": "Extract Disease History with Multiple Source Comparisons", "topics": [ "extract_disease_history_sensitivity" ] }, { "page": "extract_medications", "title": "Extract medication use from UKB drug fields", "topics": [ "extract_medications" ] }, { "page": "extract_self_report_medications", "title": "Extract self-reported medication indicators from field 20003", "topics": [ "extract_self_report_medications" ] }, { "page": "fit_mi_models", "title": "Fit Regression Models on Multiple Imputed Datasets", "topics": [ "fit_mi_models" ] }, { "page": "get_death_dates", "title": "Extract Death Dates for All Deceased Participants", "topics": [ "get_death_dates" ] }, { "page": "get_disease_catalog", "title": "Query the built-in disease code catalog", "topics": [ "get_disease_catalog" ] }, { "page": "get_field_info", "title": "Get one UK Biobank field's metadata", "topics": [ "get_field_info" ] }, { "page": "get_field_metadata", "title": "Get structured UK Biobank field metadata", "topics": [ "get_field_metadata" ] }, { "page": "get_medication_catalog", "title": "Query the built-in medication code catalog", "topics": [ "get_medication_catalog" ] }, { "page": "get_pomegranate_diseases", "title": "Get Pomegranate-derived disease definitions", "topics": [ "get_pomegranate_diseases" ] }, { "page": "get_pomegranate_source_manifest", "title": "Get the Pomegranate source manifest", "topics": [ "get_pomegranate_source_manifest" ] }, { "page": "get_predefined_diseases", "title": "Get Predefined Disease Definitions", "topics": [ "get_predefined_diseases" ] }, { "page": "get_predefined_medications", "title": "Get predefined UK Biobank medication definitions", "topics": [ "get_predefined_medications" ] }, { "page": "get_protein_ppi", "title": "Retrieve a STRING PPI network for proteomics hits", "topics": [ "get_protein_ppi" ] }, { "page": "get_ukb_demo_colnames", "title": "Get column names of the synthetic UK Biobank-style demo dataset", "topics": [ "get_ukb_demo_colnames" ] }, { "page": "get_variable_info", "title": "Get information about available variables", "topics": [ "get_variable_info" ] }, { "page": "get_variable_set", "title": "Get one curated UK Biobank variable set", "topics": [ "get_variable_set" ] }, { "page": "get_variable_sets", "title": "Curated UK Biobank variable sets for extraction", "topics": [ "get_variable_sets" ] }, { "page": "load_pomegranate_portal_coding", "title": "Load the Pomegranate portal coding evidence table", "topics": [ "load_pomegranate_portal_coding" ] }, { "page": "load_ukb_medication_coding", "title": "Load UK Biobank field 20003 medication coding", "topics": [ "load_ukb_medication_coding" ] }, { "page": "load_ukb_metabolite_panel", "title": "Load the bundled UK Biobank non-ratio metabolite panel", "topics": [ "load_ukb_metabolite_panel" ] }, { "page": "match_propensity", "title": "Propensity Score Matching", "topics": [ "match_propensity" ] }, { "page": "metabolite_to_metaboanalyst_name", "title": "Map metabolite names to MetaboAnalyst-compatible names", "topics": [ "metabolite_to_metaboanalyst_name" ] }, { "page": "parse_cancer_registry", "title": "Parse Cancer Registry Records", "topics": [ "parse_cancer_registry" ] }, { "page": "parse_death_records", "title": "Parse Death Registry Records", "topics": [ "parse_death_records" ] }, { "page": "parse_icd10_diagnoses", "title": "Parse ICD-10 Hospital Diagnosis Records", "topics": [ "parse_icd10_diagnoses" ] }, { "page": "parse_icd9_diagnoses", "title": "Parse ICD-9 Hospital Diagnosis Records", "topics": [ "parse_icd9_diagnoses" ] }, { "page": "parse_opcs4_procedures", "title": "Parse OPCS4 Hospital Procedure Records", "topics": [ "parse_opcs4_procedures" ] }, { "page": "parse_self_reported_illnesses", "title": "Parse Self-Reported Illness Records", "topics": [ "parse_self_reported_illnesses" ] }, { "page": "plot_balance", "title": "Plot Covariate Balance (Love Plot)", "topics": [ "plot_balance" ] }, { "page": "plot_calibration", "title": "Plot Calibration Curve", "topics": [ "plot_calibration" ] }, { "page": "plot_correlation", "title": "Visualize correlation matrix as a heatmap", "topics": [ "plot_correlation" ] }, { "page": "plot_cox_loghr_correlation", "title": "Plot training-validation Cox log(HR) concordance", "topics": [ "plot_cox_loghr_correlation" ] }, { "page": "plot_cox_sensitivity_correlation", "title": "Plot sensitivity-analysis Cox log(HR) concordance", "topics": [ "plot_cox_sensitivity_correlation" ] }, { "page": "plot_enrichment_lollipop", "title": "Plot enrichment results as a lollipop chart via TCMDATA", "topics": [ "plot_enrichment_lollipop" ] }, { "page": "plot_forest", "title": "Plot Forest Plot for Subgroup Analysis", "topics": [ "plot_forest" ] }, { "page": "plot_go_ora_bar", "title": "Plot GO ORA results as a bar chart via TCMDATA", "topics": [ "plot_go_ora_bar" ] }, { "page": "plot_heatmap", "title": "Plot a publication-style heatmap", "topics": [ "plot_heatmap" ] }, { "page": "plot_km_curve", "title": "Plot Kaplan-Meier Survival Curve", "topics": [ "plot_km_curve" ] }, { "page": "plot_mediation", "title": "Plot Mediation Analysis Results", "topics": [ "plot_mediation" ] }, { "page": "plot_mediation_forest", "title": "Plot Forest Plot for Multiple Mediator Analysis", "topics": [ "plot_mediation_forest" ] }, { "page": "plot_metabolite_ora_barplot", "title": "Plot metabolite ORA results as a bar plot", "topics": [ "plot_metabolite_ora_barplot" ] }, { "page": "plot_metabolite_ora_dotplot", "title": "Plot metabolite ORA results as a dot plot", "topics": [ "plot_metabolite_ora_dotplot" ] }, { "page": "plot_mi_diagnostics", "title": "Plot Multiple Imputation Diagnostics", "topics": [ "plot_mi_diagnostics" ] }, { "page": "plot_mi_pooled", "title": "Plot Multiple Imputation Pooled Results", "topics": [ "plot_mi_pooled" ] }, { "page": "plot_ml_calibration", "title": "Plot Calibration Curve", "topics": [ "plot_ml_calibration" ] }, { "page": "plot_ml_compare", "title": "Plot Model Comparison", "topics": [ "plot_ml_compare" ] }, { "page": "plot_ml_confusion", "title": "Plot Confusion Matrix", "topics": [ "plot_ml_confusion" ] }, { "page": "plot_ml_dca", "title": "Plot Decision Curve Analysis", "topics": [ "plot_ml_dca" ] }, { "page": "plot_ml_gain", "title": "Plot Gain Curve", "topics": [ "plot_ml_gain" ] }, { "page": "plot_ml_importance", "title": "Plot Variable Importance", "topics": [ "plot_ml_importance" ] }, { "page": "plot_ml_ks", "title": "Plot KS Curve", "topics": [ "plot_ml_ks" ] }, { "page": "plot_ml_lift", "title": "Plot Lift Curve", "topics": [ "plot_ml_lift" ] }, { "page": "plot_ml_pr", "title": "Plot PR Curve", "topics": [ "plot_ml_pr" ] }, { "page": "plot_ml_roc", "title": "Plot ROC Curves", "topics": [ "plot_ml_roc" ] }, { "page": "plot_ml_roc_compare", "title": "Plot One or More ROC Curves from Tidy ROC Data", "topics": [ "plot_ml_roc_compare" ] }, { "page": "plot_participant_flow", "title": "Plot a participant flow table", "topics": [ "plot_participant_flow" ] }, { "page": "plot_ps_distribution", "title": "Plot Propensity Score Distribution", "topics": [ "plot_ps_distribution" ] }, { "page": "plot_rcs", "title": "Plot a restricted cubic spline exposure-response curve", "topics": [ "plot.ukb_rcs", "plot_rcs", "plot_rcs.ukb_rcs" ] }, { "page": "plot_regression_volcano", "title": "Plot a volcano-style regression summary", "topics": [ "plot_regression_volcano" ] }, { "page": "plot_scatter", "title": "Plot a publication-style scatter plot", "topics": [ "plot_scatter" ] }, { "page": "plot_shap_beeswarm", "title": "Plot SHAP Beeswarm Summary", "topics": [ "plot_shap_beeswarm" ] }, { "page": "plot_shap_dependence", "title": "Plot SHAP Dependence", "topics": [ "plot_shap_dependence" ] }, { "page": "plot_shap_force", "title": "Plot SHAP Force (Waterfall)", "topics": [ "plot_shap_force" ] }, { "page": "plot_shap_summary", "title": "Plot SHAP Summary", "topics": [ "plot_shap_summary" ] }, { "page": "plot_stacked_bar", "title": "Plot a publication-style stacked bar chart", "topics": [ "plot_stacked_bar" ] }, { "page": "plot_top_hr_bars", "title": "Plot top positive and inverse Cox associations", "topics": [ "plot_top_hr_bars" ] }, { "page": "plot_violin", "title": "Plot a publication-style violin plot", "topics": [ "plot_violin" ] }, { "page": "plot.ukb_ml_flow", "title": "Plot a UKB ML Flow Object", "topics": [ "plot.ukb_ml_flow" ] }, { "page": "plot.ukb_ml_flow_compare", "title": "Plot a UKB ML Flow Comparison Object", "topics": [ "plot.ukb_ml_flow_compare" ] }, { "page": "pool_custom_estimates", "title": "Pool Custom Estimates from Multiple Imputations", "topics": [ "pool_custom_estimates" ] }, { "page": "pool_mi_models", "title": "Pool Results from Multiple Imputation Models", "topics": [ "pool_mi_models" ] }, { "page": "preprocess_baseline", "title": "Preprocess UKB baseline variables", "topics": [ "preprocess_baseline" ] }, { "page": "print.mediation_result", "title": "Print Method for Mediation Results", "topics": [ "print.mediation_result" ] }, { "page": "protein_to_gene_symbol", "title": "Convert protein identifiers to gene symbols", "topics": [ "protein_to_gene_symbol" ] }, { "page": "rank_protein_ppi_nodes", "title": "Rank nodes in a PPI network by integrated centrality", "topics": [ "rank_protein_ppi_nodes" ] }, { "page": "rap_extract_pheno", "title": "Extract RAP Phenotype Data Synchronously", "topics": [ "rap_extract_pheno" ] }, { "page": "rap_find_dataset", "title": "Find the RAP Dataset File in the Current Project", "topics": [ "rap_find_dataset" ] }, { "page": "rap_list_fields", "title": "List Approved RAP Dataset Fields", "topics": [ "rap_list_fields" ] }, { "page": "rap_plan_extract", "title": "Plan a RAP Phenotype Extraction", "topics": [ "rap_plan_extract" ] }, { "page": "rap_submit_extract", "title": "Submit a RAP Table-Exporter Phenotype Extraction Job", "topics": [ "rap_submit_extract" ] }, { "page": "run_correlation", "title": "Calculate correlation between variables", "topics": [ "run_correlation" ] }, { "page": "run_imputation", "title": "Multiple imputation and merge back to full data", "topics": [ "run_imputation" ] }, { "page": "run_mediation", "title": "Run Causal Mediation Analysis", "topics": [ "run_mediation" ] }, { "page": "run_metabolite_ora", "title": "Run metabolite over-representation analysis", "topics": [ "run_metabolite_ora" ] }, { "page": "run_multi_mediator", "title": "Run Multiple Mediator Analysis", "topics": [ "run_multi_mediator" ] }, { "page": "run_multi_subgroup", "title": "Run Multiple Subgroup Analyses", "topics": [ "run_multi_subgroup" ] }, { "page": "run_protein_kegg_ora", "title": "Run KEGG ORA enrichment for proteomics hits", "topics": [ "run_protein_kegg_ora" ] }, { "page": "run_protein_ora", "title": "Run GO ORA enrichment for proteomics hits", "topics": [ "run_protein_ora" ] }, { "page": "run_protein_ppi_clustering", "title": "Cluster a protein-protein interaction network", "topics": [ "run_protein_ppi_clustering" ] }, { "page": "run_protein_ppi_robustness", "title": "Evaluate PPI network robustness for selected protein targets", "topics": [ "run_protein_ppi_robustness" ] }, { "page": "run_rcs", "title": "Fit a restricted cubic spline exposure-response model", "topics": [ "run_rcs" ] }, { "page": "run_regression", "title": "Run a regression model (unified interface)", "topics": [ "run_regression" ] }, { "page": "run_sensitivity_mediation", "title": "Sensitivity Analysis for Mediation", "topics": [ "run_sensitivity_mediation" ] }, { "page": "run_subgroup_analysis", "title": "Run Subgroup Analysis", "topics": [ "run_subgroup_analysis" ] }, { "page": "run_weighted_analysis", "title": "Run Weighted Analysis", "topics": [ "run_weighted_analysis" ] }, { "page": "runmulti_competing", "title": "Run Multiple Fine-Gray Competing-Risk Models", "topics": [ "runmulti_competing" ] }, { "page": "runmulti_cox", "title": "Run multiple Cox proportional hazards models", "topics": [ "runmulti_cox" ] }, { "page": "runmulti_cox_lag", "title": "Run Lagged Cox Sensitivity Analyses", "topics": [ "runmulti_cox_lag" ] }, { "page": "runmulti_cox_zph", "title": "Run Multiple Cox Models with PH Diagnostics", "topics": [ "runmulti_cox_zph" ] }, { "page": "runmulti_gam", "title": "Run multiple generalised additive models", "topics": [ "runmulti_gam" ] }, { "page": "runmulti_glm", "title": "Run multiple generalised linear models", "topics": [ "runmulti_glm" ] }, { "page": "runmulti_lm", "title": "Run multiple linear regression models", "topics": [ "runmulti_lm" ] }, { "page": "runmulti_logit", "title": "Run multiple logistic regression models", "topics": [ "runmulti_logit" ] }, { "page": "runmulti_negbin", "title": "Run multiple negative-binomial regression models", "topics": [ "runmulti_negbin" ] }, { "page": "runmulti_trend", "title": "Run Grouped-Exposure Trend Tests", "topics": [ "runmulti_trend" ] }, { "page": "score_protein_ppi_clusters", "title": "Score network clusters in a PPI graph", "topics": [ "score_protein_ppi_clusters" ] }, { "page": "select_incident_by_years", "title": "Select Incident Cases by Time Since Enrollment", "topics": [ "select_incident_by_years" ] }, { "page": "sensitivity_exclude_early_events", "title": "Exclude Early Events for Sensitivity Analysis", "topics": [ "sensitivity_exclude_early_events" ] }, { "page": "sensitivity_exclude_missing_covariates", "title": "Exclude Rows with Missing Covariates for Sensitivity Analysis", "topics": [ "sensitivity_exclude_missing_covariates" ] }, { "page": "subset_protein_ppi", "title": "Filter a STRING PPI network via TCMDATA", "topics": [ "subset_protein_ppi" ] }, { "page": "summary.mediation_result", "title": "Summary Method for Mediation Results", "topics": [ "summary.mediation_result" ] }, { "page": "tidy.mi_pooled_result", "title": "Tidy Method for mi_pooled_result", "topics": [ "tidy.mi_pooled_result" ] }, { "page": "ukb_check_rap_env", "title": "Check the UK Biobank RAP execution environment", "topics": [ "ukb_check_rap_env" ] }, { "page": "ukb_clean_missing", "title": "Clean UK Biobank Missing and Non-response Values", "topics": [ "ukb_clean_missing" ] }, { "page": "ukb_compare_cox_results", "title": "Compare Cox results between training and validation sets", "topics": [ "ukb_compare_cox_results" ] }, { "page": "ukb_compare_sensitivity_cox", "title": "Compare sensitivity Cox results against a main analysis", "topics": [ "ukb_compare_sensitivity_cox" ] }, { "page": "ukb_cox_diagnostics", "title": "Diagnose Proportional Hazards Assumptions for a Cox Model", "topics": [ "ukb_cox_diagnostics" ] }, { "page": "ukb_create_extraction_manifest", "title": "Create a RAP extraction manifest", "topics": [ "ukb_create_extraction_manifest" ] }, { "page": "ukb_decode", "title": "Decode UK Biobank RAP exports", "topics": [ "ukb_decode" ] }, { "page": "ukb_decode_column_names", "title": "Decode UK Biobank column names", "topics": [ "ukb_decode_column_names" ] }, { "page": "ukb_decode_values", "title": "Decode UK Biobank coded values", "topics": [ "ukb_decode_values" ] }, { "page": "ukb_demo", "title": "Generate a small synthetic UK Biobank-style demo dataset", "topics": [ "ukb_demo" ] }, { "page": "ukb_dictionary_zh", "title": "Chinese UK Biobank field-path dictionary", "topics": [ "ukb_dictionary_zh" ] }, { "page": "ukb_download_rap_dictionary", "title": "Download the official RAP data dictionary", "topics": [ "ukb_download_rap_dictionary" ] }, { "page": "ukb_extract_fields", "title": "Extract UK Biobank fields from a search result or field list", "topics": [ "ukb_extract_fields" ] }, { "page": "ukb_field_info", "title": "Inspect one UK Biobank field", "topics": [ "ukb_field_info" ] }, { "page": "ukb_metadata_setup", "title": "Set up UK Biobank metadata for search, extraction, and decoding", "topics": [ "ukb_metadata_setup" ] }, { "page": "ukb_ml_as_split", "title": "Standardize Manual ML Train/Test Splits", "topics": [ "ukb_ml_as_split" ] }, { "page": "ukb_ml_calibration", "title": "Calibration Curve Analysis", "topics": [ "ukb_ml_calibration" ] }, { "page": "ukb_ml_compare", "title": "Compare Multiple ML Models", "topics": [ "ukb_ml_compare" ] }, { "page": "ukb_ml_compare_feature_sets", "title": "Compare Multiple Feature Sets with a Frozen-Test ML Workflow", "topics": [ "ukb_ml_compare_feature_sets" ] }, { "page": "ukb_ml_compare_flows", "title": "Compare Multiple Feature Sets and/or Models", "topics": [ "ukb_ml_compare_flows" ] }, { "page": "ukb_ml_confusion", "title": "Confusion Matrix", "topics": [ "ukb_ml_confusion" ] }, { "page": "ukb_ml_cv", "title": "Cross-Validation for ML Models", "topics": [ "ukb_ml_cv" ] }, { "page": "ukb_ml_dca", "title": "Decision Curve Analysis", "topics": [ "ukb_ml_dca" ] }, { "page": "ukb_ml_evaluate_test", "title": "Evaluate the Final Model Once on the Frozen Test Set", "topics": [ "ukb_ml_evaluate_test" ] }, { "page": "ukb_ml_feature_select", "title": "Select Features for UKB ML Workflows", "topics": [ "ukb_ml_feature_select" ] }, { "page": "ukb_ml_fit_final", "title": "Refit the Final ML Model on Training Development Data", "topics": [ "ukb_ml_fit_final" ] }, { "page": "ukb_ml_flow", "title": "Run a Complete Single-Model UKB ML Flow", "topics": [ "ukb_ml_flow" ] }, { "page": "ukb_ml_gain_lift", "title": "Gain and Lift Curve Analysis", "topics": [ "ukb_ml_gain_lift" ] }, { "page": "ukb_ml_importance", "title": "Get Variable Importance", "topics": [ "ukb_ml_importance" ] }, { "page": "ukb_ml_ks", "title": "KS Curve Analysis", "topics": [ "ukb_ml_ks" ] }, { "page": "ukb_ml_metrics", "title": "Calculate Model Performance Metrics", "topics": [ "ukb_ml_metrics" ] }, { "page": "ukb_ml_model", "title": "Train a Machine Learning Model", "topics": [ "ukb_ml_model" ] }, { "page": "ukb_ml_pr", "title": "Precision-Recall Curve Analysis", "topics": [ "ukb_ml_pr" ] }, { "page": "ukb_ml_predict", "title": "Predict from ML Model", "topics": [ "ukb_ml_predict" ] }, { "page": "ukb_ml_roc", "title": "ROC Curve Analysis", "topics": [ "ukb_ml_roc" ] }, { "page": "ukb_ml_roc_data", "title": "Create ROC Curve Data for Binary ML Predictions", "topics": [ "ukb_ml_roc_data" ] }, { "page": "ukb_ml_split_data", "title": "Split Data into Frozen ML Train/Test Sets", "topics": [ "ukb_ml_split_data" ] }, { "page": "ukb_ml_supported_models", "title": "List Supported Machine Learning Models", "topics": [ "ukb_ml_supported_models" ] }, { "page": "ukb_ml_survival", "title": "Train Survival Machine Learning Model", "topics": [ "ukb_ml_survival" ] }, { "page": "ukb_ml_survival_as_split", "title": "Standardize Manual Survival ML Train/Test Splits", "topics": [ "ukb_ml_survival_as_split" ] }, { "page": "ukb_ml_survival_evaluate_test", "title": "Evaluate Survival ML Once on the Frozen Test Set", "topics": [ "ukb_ml_survival_evaluate_test" ] }, { "page": "ukb_ml_survival_feature_select", "title": "Select Features for Survival ML Workflows", "topics": [ "ukb_ml_survival_feature_select" ] }, { "page": "ukb_ml_survival_fit_final", "title": "Refit Final Survival ML Model", "topics": [ "ukb_ml_survival_fit_final" ] }, { "page": "ukb_ml_survival_importance", "title": "Get Variable Importance for Survival Model", "topics": [ "ukb_ml_survival_importance" ] }, { "page": "ukb_ml_survival_predict", "title": "Predict from Survival ML Model", "topics": [ "ukb_ml_survival_predict" ] }, { "page": "ukb_ml_survival_shap", "title": "SHAP Values for Survival Models", "topics": [ "ukb_ml_survival_shap" ] }, { "page": "ukb_ml_survival_split_data", "title": "Split Data into Frozen Survival ML Train/Test Sets", "topics": [ "ukb_ml_survival_split_data" ] }, { "page": "ukb_ml_survival_tune", "title": "Tune Survival ML Hyperparameters Without Touching the Test Set", "topics": [ "ukb_ml_survival_tune" ] }, { "page": "ukb_ml_survival_workflow", "title": "Run a Frozen-Test Survival ML Workflow", "topics": [ "ukb_ml_survival_workflow" ] }, { "page": "ukb_ml_threshold", "title": "Learn a Binary Classification Threshold", "topics": [ "ukb_ml_threshold" ] }, { "page": "ukb_ml_tune", "title": "Tune ML Hyperparameters Without Touching the Test Set", "topics": [ "ukb_ml_tune" ] }, { "page": "ukb_ml_workflow", "title": "Run a Frozen-Test UKB ML Workflow", "topics": [ "ukb_ml_workflow" ] }, { "page": "ukb_participant_flow", "title": "Build a participant flow table", "topics": [ "ukb_participant_flow" ] }, { "page": "ukb_protein_annotation", "title": "Annotate Olink-style protein variables", "topics": [ "ukb_protein_annotation" ] }, { "page": "ukb_query_dictionary", "title": "Query UK Biobank dictionary metadata", "topics": [ "ukb_query_dictionary" ] }, { "page": "ukb_scale_with_parameters", "title": "Standardize variables using existing scaling parameters", "topics": [ "ukb_scale_with_parameters" ] }, { "page": "ukb_search_fields", "title": "Search UK Biobank fields", "topics": [ "ukb_search_fields" ] }, { "page": "ukb_sensitivity_suite", "title": "Run a Cox sensitivity-analysis suite", "topics": [ "ukb_sensitivity_suite" ] }, { "page": "ukb_shap", "title": "Compute SHAP Values", "topics": [ "ukb_shap" ] }, { "page": "ukb_shap_dependence", "title": "SHAP Dependence Values", "topics": [ "ukb_shap_dependence" ] }, { "page": "ukb_shap_force", "title": "SHAP Force Plot Data", "topics": [ "ukb_shap_force" ] }, { "page": "ukb_shap_summary", "title": "SHAP Summary Statistics", "topics": [ "ukb_shap_summary" ] }, { "page": "ukb_snapshot", "title": "Record or Retrieve UKB Cohort Snapshots", "topics": [ "ukb_snapshot" ] }, { "page": "ukb_standardize_by_train", "title": "Standardize variables using training-set parameters", "topics": [ "ukb_standardize_by_train" ] }, { "page": "ukb_time_skeleton", "title": "Build a UK Biobank follow-up time skeleton", "topics": [ "ukb_time_skeleton" ] }, { "page": "ukb_top_hr_results", "title": "Select top Cox associations by hazard ratio", "topics": [ "ukb_top_hr_results" ] }, { "page": "ukb_train_validation_cox", "title": "Run Cox models in training and validation sets", "topics": [ "ukb_train_validation_cox" ] }, { "page": "ukb_validate_columns", "title": "Validate requested columns against a data object", "topics": [ "ukb_validate_columns" ] }, { "page": "ukb_write_extraction_manifest", "title": "Write a RAP extraction manifest", "topics": [ "ukb_write_extraction_manifest" ] }, { "page": "UKBAnalytica", "title": "UKBAnalytica: UK Biobank Data Processing and Survival Analysis Toolkit", "topics": [ "UKBAnalytica-package", "UKBAnalytica" ] } ], "_pkglogo": "https://github.com/cran/UKBAnalytica/raw/HEAD/man/figures/logo.png", "_rundeps": [ "backports", "bit", "bit64", "boot", "broom", "class", "cli", "clipr", "codetools", "cpp11", "crayon", "data.table", "DBI", "dplyr", "e1071", "farver", "forcats", "foreach", "gdata", "generics", "ggplot2", "glmnet", "glue", "gmodels", "gtable", "gtools", "haven", "hms", "igraph", "isoband", "iterators", "jomo", "labeling", "labelled", "lattice", "lifecycle", "lme4", "lmtest", "magrittr", "MASS", "Matrix", "mgcv", "mice", "minqa", "mitml", "mitools", "nlme", "nloptr", "nnet", "numDeriv", "ordinal", "pan", "pillar", "pkgconfig", "prettyunits", "pROC", "progress", "proxy", "purrr", "R6", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "Rdpack", "readr", "reformulas", "rlang", "rpart", "S7", "sandwich", "scales", "shape", "stringi", "stringr", "survey", "survival", "tableone", "tibble", "tidyr", "tidyselect", "tzdb", "ucminf", "utf8", "vctrs", "viridisLite", "vroom", "withr", "xml2", "zoo" ], "_score": 1.6334684555795866, "_indexed": true, "_nocasepkg": "ukbanalytica", "_universes": [ "cran", "mysteriouslaolong", "hinna0818" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.0.0", "date": "2026-06-30T13:27:38.000Z", "distro": "resolute", "commit": "38137fe91b0c96d2b354e05d431568df3416cab3", "fileid": "bea2d40515a4c9a31aa796e4b599dc025d9d2d1ffc099ba9d88f527daecec733", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/28447327359" }, { "r": "4.6.1", "os": "linux", "version": "1.0.0", "date": "2026-06-30T13:27:34.000Z", "distro": "resolute", "commit": "38137fe91b0c96d2b354e05d431568df3416cab3", "fileid": "9c354828958eb61cd996613e126117c6d7c1a537eaeb70a175b9df7b4e8dd0f5", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/28447327359" }, { "r": "4.6.0", "os": "wasm", "version": "1.0.0", "date": "2026-06-30T13:28:22.000Z", "commit": "38137fe91b0c96d2b354e05d431568df3416cab3", "fileid": "2d3a532488f2ccabfef17b855961464013dcba4460f97150631f82998c58e941", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/28447327359" } ] }