https://sbresnahan.r-universe.devRecent package updates in sbresnahanR-universehttps://github.com/sbresnahan.pnghttps://sbresnahan.r-universe.devWed, 19 Aug 2026 18:07:21 GMTseantbresnahan3@gmail.com (Sean T. Bresnahan)Provides a model selection workflow for causal inference with genetic instruments and negative controls in observational omics data. The package fits eight estimators of the natural direct and indirect effects (NDE/NIE), diagnoses which are valid for the user's data via instrument-strength checks and negative-control validity screens (A1, A2, A2'), stress-tests them against confounding and pleiotropy violations, and recommends the estimator most likely to be unbiased. A hybrid generative texture model (a torch GAN for sample-level structure and a Gaussian copula for the mediator panel) lets the sensitivity analysis mirror the marginal and joint structure of the user's cohort. The modular API comprises iconic_data() to standardize data, iconic_diagnose() to assess estimator eligibility, iconic_estimate() to fit all eligible estimators, iconic_sensitivity() to sweep instrument exogeneity, iconic_recommend() to rank estimators by identification strength and robustness, and iconic_prospect() to plan future studies. infer_confounding() estimates held-fixed confounding parameters from the data. Helper functions construct exposure instruments (polygenic scores from GWAS summary statistics or PGS Catalog scoring files), mediator instruments (cis-eQTL scans and elastic-net predicted-expression composites), and negative-control panels (principal components of residualized omics matrices), and data can be imported directly from SummarizedExperiment objects. Time-to-event outcomes are supported on the Cox log-hazard-ratio and restricted-mean-survival-time scales. Binary outcomes are supported on the log-odds-ratio (logistic two-stage predictor-substitution outcome stage) and risk-difference (linear probability model) scales. The torch package is required for the generative texture model (sensitivity and prospective analysis); all other functionality works without it.https://github.com/r-universe/biocstaging/actions/runs/39114166084Wed, 19 Aug 2026 18:07:21 GMTiconic0.99.3successhttps://biocstaging.r-universe.devhttps://github.com/BiocStaging/iconiciconic-diagnose.Rmdiconic-diagnose.htmlDiagnosing Estimator Eligibility2026-07-26 15:26:112026-08-12 15:28:48iconic-instruments.Rmdiconic-instruments.htmlGenetic Instruments and Negative Controls from External Data2026-08-12 15:28:482026-08-12 15:28:48iconic-walkthrough.Rmdiconic-walkthrough.htmlICONIC: Causal Model Selection with Genetic Instruments and Negative Controls2026-07-22 16:36:082026-08-12 15:28:48iconic-sensitivity.Rmdiconic-sensitivity.htmlSensitivity Analysis and Confounding Inference2026-07-26 15:26:112026-08-12 15:28:48iconic-survival.Rmdiconic-survival.htmlSurvival Outcomes2026-07-26 15:26:112026-08-12 15:28:48iconic-texture-model.Rmdiconic-texture-model.htmlThe Generative Texture Model2026-07-26 15:26:112026-08-12 15:28:48