choxos

Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.

Adaptive trial designs in R, including platform, basket, MAMS, response-adaptive, and interim decision methods.

R package documentation with roxygen2 and pkgdown, including reference topics, articles, and site configuration.

Hyperparameter tuning in tidymodels with grids, Bayesian optimization, racing, and workflow finalization.

MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.

Genomics analysis in R with Bioconductor, differential expression, enrichment, batch correction, and single-cell workflows.

Common clinical trial design patterns including multi-arm, multi-endpoint, adaptive, and stratified designs. Use when selecting or implementing trial designs.

Review patterns for tidymodels workflows, including leakage, resampling, tuning, metrics, and reproducibility.

Resampling strategies in tidymodels, including validation splits, cross-validation, bootstrap, nested resampling, and grouped data.