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• Documentation of Bayesian Data Analysis Basics

  The repository currently focuses on methods for statistical causal inference (SCI). To unfold its proper potential, the same attention should be dedicated to Bayesian data analysis (BDA), the second side of the medal that complements SCI. The documentation of BDA should contain both (1) notebooks of R code plus (2) lecture slides for teaching those concepts. The concepts should include: ## Basic skills - Specifying a regression model in brms. - Evaluating the training of a regression model in brms. - Prior and posterior checks of regression models. - Visualizing posterior distributions of a trained model in brms. - Plotting marginal effects of brms models. ## Application skills - Specifying regression models for different outcome distributions, including the Normal, Binomial, Poisson & Negative Binomial, Ordered Logit, and Beta distributions. - Specifying interaction effects in regression models. - Specifying random effects in regression models. - Specifying and evaluating multivariate regression models in brms.

  Due by June 20, 2026

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• Conference Tutorial

  This version of the BDA4SCI educational material should be fit for a tutorial presentation at a software engineering conference. In addition to the prototype, it shall contain (1) more realistic examples from the actual literature of the research field, (2) a clearer structure of concepts (including SCI, SCM, BDA, PO, etc.), and (3) more concepts (including d-separation, model comparison, interaction, etc.).

  Overdue by 11 month(s)

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  Due by April 28, 2025

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