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Released: April 2026
grs_validate() — added
skip_if_not_installed("pROC") guard to all logistic-path
tests so the suite passes cleanly when pROC is absentops_toy() data where possible — runnable
examples unwrapped, long-running examples (>5s) wrapped in
\donttest{}, network-dependent examples remain in
\dontrun{}VignetteBuilder field corrected in
DESCRIPTIONdev (3
jobs), main (6 jobs), release (8 jobs,
--as-cran), and weekly (scheduled every
Saturday 21:00 CST, full CRAN-like matrix)_R_CHECK_DEPENDS_ONLY_ matrix to verify Suggests
packages are correctly declaredReleased: March 26, 2026
ops_snapshot() — removed Unicode character
Δ from documentation to fix LaTeX PDF manual build on
CRANtest-plot.R — added skip_on_cran() to
plot_tableone() rendering tests that caused a 20-minute
hang on Windows CRANplot_forest() and plot_tableone() examples
are now fully runnable (removed \dontrun{} wrapper;
save = FALSE throughout)README.md — updated Codecov badge URL; fixed
CONTRIBUTING.md and README_zh.md links to full
GitHub URLsinst/WORDLIST with Biobank to
suppress false-positive spelling NOTE on CRANReleased: March 25, 2026
derive_followup() — coerce date columns to
IDate before pmin() to avoid type
mismatchinstall.Rmd — corrected vignette cross-references.assert_*() input
validation helperscli::cli_abort() calls now use
call = NULL for cleaner error messagesplot_tableone() — auto-coerce data.table
input to data.frameget-started.Rmd and README updated for
accuracyReleased: March 13, 2026
ops_withdraw() — exclude UKB withdrawn participants
from a cohort data.table by EIDops_snapshot() gains column-tracking helpers:
cols(), diff(), remove(), and
set_safe_cols()plot_tableone() — new png_scale,
pdf_width, and pdf_height parameters for
fine-grained output controlfetch_file() — enforce RAP-only guard; updated
testsgrs_score() — fix -icmd argument format;
skip script upload if file already exists on RAPops_withdraw()ops_withdraw()broom to DESCRIPTION Imports and
ops_setup() dependency checkReleased: March 10, 2026
ops_setup() — check and report the local environment
(R, dx-toolkit, dxpy) healthops_toy() — generate synthetic UKB-style cohort or
forest-plot data for testing and demos; includes GRS columns and cancer
self-report fieldsops_na() — summarise missing-value rates per column
with threshold-based filtering and cli progress
feedbackops_snapshot() — record and display a history of
dataset row/column counts across pipeline stepscli::cli_abort() calls now pass
call = NULL to suppress internal call-stack noise in error
messagesops_toy(): added cancer self-report fields
(p20001, p20006) and corrected
sr_codes → text label mappingvignette("smoking_lung_cancer"))docs/)importFrom(stats, rnorm, runif),
importFrom(utils, object.size), and pct_na to
globalVariables()builds/ to .RbuildignoreReleased: March 6, 2026
Initial release of ukbflow — a RAP-native R workflow for UK Biobank analysis.
auth_login() — authenticate to RAP via dx-toolkit
tokenauth_logout() — revoke current sessionauth_status() — check current login stateauth_list_projects() — list accessible RAP
projectsauth_select_project() — set active RAP projectfetch_ls() / fetch_tree() — browse RAP
project file structurefetch_file() — download files from RAP to localfetch_url() — generate pre-signed download URLsfetch_metadata() — retrieve UKB field metadata
(field.tsv, encoding.tsv)fetch_field() — retrieve UKB field-level metadata for
specific field IDsextract_ls() — list available UKB datasets on RAPextract_pheno() — synchronously extract phenotype
fields from a RAP datasetextract_batch() — submit a DNAnexus table-exporter job
to extract phenotype fieldsjob_wait() — poll job status until completionjob_status() / job_ls() /
job_path() / job_result() — monitor and locate
RAP jobsdecode_values() — convert integer codes to
human-readable labelsdecode_names() — rename
p{field}_i{instance}_a{array} columns to descriptive
namesderive_missing() — recode UKB informative-missing
labels to NAderive_covariate() — standardise common covariates
(age, BMI, TDI, etc.)derive_cut() — create ordered factor variables from
numeric cutpointsderive_hes() — derive disease phenotypes from Hospital
Episode Statistics (ICD-10)derive_cancer_registry() — derive cancer phenotypes
from UK cancer registry (ICD-10)derive_death_registry() — derive phenotypes from death
registry (primary + secondary causes)derive_first_occurrence() — derive phenotypes from UKB
First Occurrence fieldsderive_selfreport() — derive disease phenotypes from
self-reported illness codesderive_icd10() — merge multi-source ICD-10 case
definitions across all registersderive_case() — merge arbitrary multi-source case
definitionsderive_followup() — compute follow-up time with
competing event supportderive_timing() — classify prevalent vs. incident
casesderive_age() — compute age at eventassoc_linear() — linear regression with automatic
three-model frameworkassoc_logistic() — logistic regression with automatic
three-model frameworkassoc_coxph() — Cox proportional hazards with automatic
three-model frameworkassoc_coxph_zph() — test proportional hazards
assumptionassoc_competing() — Fine-Gray competing risks
regressionassoc_subgroup() — subgroup analysis with interaction
likelihood-ratio testassoc_trend() — dose-response trend analysis across
ordered categoriesassoc_lag() — landmark / lag-time sensitivity
analysisgrs_check() — validate and reformat a GWAS summary
statistics weight filegrs_bgen2pgen() — submit parallel BGEN → PGEN
conversion jobs on RAPgrs_score() — submit distributed plink2 GRS scoring
jobs on RAPgrs_standardize() — standardise GRS columns to mean =
0, SD = 1grs_validate() — validate GRS distribution and
association with known risk factorsplot_forest() — publication-ready forest plots with
customisable CI columns and p-value formattingplot_tableone() — Table 1 baseline characteristics
figureThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.