All authors
Claude Skills by choxos
github.com/choxos45 skills5 installs89 views
- Meta AnalysisBayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.Votes: 0GitHub stars: 11
- Model DiagnosticsMCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.Votes: 0GitHub stars: 11
- Pymc FundamentalsFoundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.Votes: 0GitHub stars: 11
- Regression ModelsBayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.Votes: 0GitHub stars: 11
- Stan FundamentalsFoundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.Votes: 0GitHub stars: 11
- Survival ModelsBayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.Votes: 0GitHub stars: 11
- Time Series ModelsBayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.Votes: 0GitHub stars: 11
- Clinical Trial Design PatternsCommon clinical trial design patterns including multi-arm, multi-endpoint, adaptive, and stratified designs. Use when selecting or implementing trial designs.Votes: 0GitHub stars: 11
- Group Sequential MethodsGroup sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.Votes: 0GitHub stars: 11
- Mediana FundamentalsCore Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.Votes: 0GitHub stars: 11
- Multiplicity MethodsMultiple testing procedures reference for clinical trials. Use when selecting or implementing multiplicity adjustments, gatekeeping procedures, or graphical approaches.Votes: 0GitHub stars: 11
- Power Optimization PatternsDirect and tradeoff-based optimization strategies for clinical trial design. Use when optimizing sample size, selecting design parameters, or performing sensitivity analysis.Votes: 0GitHub stars: 11
- Simtrial FundamentalsCore simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.Votes: 0GitHub stars: 11
- Time To Event MethodsSurvival analysis methods including weighted logrank, MaxCombo, RMST, and milestone tests. Use when analyzing TTE data or choosing analysis methods for non-proportional hazards.Votes: 0GitHub stars: 11
- Maic MethodologyDeep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.Votes: 0GitHub stars: 11
- Ml Nmr MethodologyDeep methodology knowledge for ML-NMR including IPD/AgD integration, population adjustment, numerical integration, and prediction to target populations. Use when conducting or reviewing ML-NMR analyses.Votes: 0GitHub stars: 11
- Nma MethodologyDeep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.Votes: 0GitHub stars: 11
- Pairwise Ma MethodologyDeep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis. Use when conducting or reviewing pairwise MA.Votes: 0GitHub stars: 11
- Stc MethodologyDeep methodology knowledge for STC including outcome regression, effect modifier selection, covariate centering, and comparison with MAIC. Use when conducting or reviewing STC analyses.Votes: 0GitHub stars: 11
- Tidy Itc WorkflowMaster tidy modelling patterns for ITC analyses following TMwR principles. Covers workflow structure, consistent interfaces, reproducibility best practices, and data validation. Use when setting up ITC analysis projects or building pipelines.Votes: 0GitHub stars: 11
- Advanced Adaptive TrialsAdaptive trial designs in R, including platform, basket, MAMS, response-adaptive, and interim decision methods.Votes: 0GitHub stars: 11
- Bayesian ModelingBayesian modeling in R with brms, rstanarm, priors, diagnostics, posterior checks, and model comparison.Votes: 0GitHub stars: 11
- Causal MediationCausal mediation analysis in R, including direct and indirect effects, assumptions, and sensitivity analysis.Votes: 0GitHub stars: 11
- Clinical TrialsClinical trial design and analysis methods in R, including randomization, estimands, multiplicity, and reporting.Votes: 0GitHub stars: 11
- Diagnostic AccuracyDiagnostic accuracy analysis in R, including sensitivity, specificity, ROC curves, likelihood ratios, and decision curves.Votes: 0GitHub stars: 11
- Epidemiology MethodsEpidemiological analysis methods in R for cohort, case-control, confounding control, and causal inference.Votes: 0GitHub stars: 11
- Genomics AnalysisGenomics analysis in R with Bioconductor, differential expression, enrichment, batch correction, and single-cell workflows.Votes: 0GitHub stars: 11
- Health EconomicsHealth economic analysis in R, including cost-effectiveness, QALYs, decision models, and budget impact.Votes: 0GitHub stars: 11
- Ipd Meta AnalysisIndividual participant data meta-analysis in R, including one-stage, two-stage, survival, and IPD with aggregate data.Votes: 0GitHub stars: 11
- Mendelian RandomizationMendelian randomization in R, including instrument selection, two-sample MR, pleiotropy checks, and sensitivity analysis.Votes: 0GitHub stars: 11
- Model EvaluationModel evaluation in R with performance metrics, calibration, ROC analysis, decision curves, and validation.Votes: 0GitHub stars: 11
- Model TuningHyperparameter tuning in tidymodels with grids, Bayesian optimization, racing, and workflow finalization.Votes: 0GitHub stars: 11
- Network Meta AnalysisNetwork meta-analysis in R, including network setup, consistency, treatment rankings, and league tables.Votes: 0GitHub stars: 11
- PharmacokineticsPharmacokinetic and pharmacodynamic analysis in R, including NCA, compartmental modeling, and bioequivalence.Votes: 0GitHub stars: 11
- R Documentation PatternsR documentation patterns with roxygen2, pkgdown, vignettes, examples, and package site structure.Votes: 0GitHub stars: 11
- Real World EvidenceReal-world evidence analysis in R, including target trial emulation, propensity scores, external controls, and bias analysis.Votes: 0GitHub stars: 11
- Recipes PatternsFeature engineering patterns with recipes, including imputation, encoding, normalization, interactions, and leakage control.Votes: 0GitHub stars: 11
- Resampling StrategiesResampling strategies in tidymodels, including validation splits, cross-validation, bootstrap, nested resampling, and grouped data.Votes: 0GitHub stars: 11
- Roxygen2 PkgdownR package documentation with roxygen2 and pkgdown, including reference topics, articles, and site configuration.Votes: 0GitHub stars: 11
- Survival AnalysisSurvival analysis in R, including Kaplan-Meier, Cox models, competing risks, RMST, and multi-state models.Votes: 0GitHub stars: 11
- Tidymodels Review PatternsReview patterns for tidymodels workflows, including leakage, resampling, tuning, metrics, and reproducibility.Votes: 0GitHub stars: 11
- Tidymodels WorkflowTidymodels workflow patterns with recipes, models, workflows, resampling, tuning, and final evaluation.Votes: 0GitHub stars: 11
- Bugs FundamentalsFoundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.Votes: 0GitHub stars: 11
- Hierarchical ModelsPatterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.Votes: 0GitHub stars: 11
- Meta AnalysisPairwise meta-analysis in R, including fixed and random effects, heterogeneity, bias checks, and forest plots.Votes: 0GitHub stars: 11