Claude Skills by CGMoreh

Define and estimate the quantity a Bayesian analysis is actually about, rather than reading it off a coefficient table. Covers stating the estimand before the model, the difference between sample and population average treatment effects, poststratifying posterior draws to a target population, computing causal contrasts by simulating each counterfactual assignment, direct and indirect effects through a mediator, and checking whether a model expansion has inflated the uncertainty of the estiman...

Write up a Bayesian analysis for a journal, so the workflow behind the estimates is visible in the paper rather than buried in an appendix. Covers the methods section stage by stage, wording for priors and their justification, reporting posteriors without significance thresholds, what belongs in the main text against the supplement, building a workflow appendix from the analysis log, checking a draft's numbers against its output, and the results section in the book's own interpretive register...

Run a Bayesian analysis in R with brms, rstanarm or cmdstanr on Stan, as an iterative workflow rather than a single fit. Covers choosing priors and checking what they imply, prior predictive simulation, MCMC failure read as a modelling problem, posterior predictive checks that name the next model, leave-one-out model comparison with pointwise attribution, prior and likelihood sensitivity by power-scaling, design calibration by simulation at the real sample size, and what weights() in a brm() ...