The qbrms Bayesian Workflow Coach

Introduction

Bayesian analysis can be intimidating. Choosing the right likelihood, setting priors, and interpreting posterior distributions often requires significant expertise.

The qbrms package includes an interactive Bayesian Workflow Coach designed to guide you through these steps. Instead of just fitting a model, the coach helps you make decisions about your analysis structure.

Launching the Coach

You can launch the assistant directly from the R console:

library(qbrms)
model_workflow_addin()

Or, if you are using RStudio, navigate to the Addins menu toolbar and select Bayesian Workflow Coach.

The 4-Stage Workflow

The add-in guides you through four distinct stages of analysis:

1. Data & Structure

  • Load Data: Import .csv, .xlsx, .rds, or SPSS/Stata files, or select a dataframe from your environment.
  • Intelligent Advice: The coach scans your response variable. Is it a proportion? A count? Positive continuous? It will suggest appropriate families (e.g., Beta, Poisson, Gamma) and warn you against common mistakes (like log-transforming raw data manually).
  • Random Effects: Automatically detects potential grouping variables for Mixed Effects modeling.

2. Priors

  • Elicitation: Instead of asking you for abstract parameter values (like “sigma” or “lambda”), the coach asks for your domain knowledge: “What is the expected mean outcome?” “What is your uncertainty?”.
  • Visual Check: It runs a Prior Predictive Check instantly. You can see exactly what data your model expects to generate before it sees your actual data. This prevents the common error of setting impossible priors.

3. Fit & Compare

  • Fit Multiple Candidates: You can select multiple families (e.g., Gaussian vs Student-t) and fit them simultaneously.
  • Model Comparison: The coach uses LOO (Leave-One-Out Cross-Validation) to compare models. It interprets the results for you, explaining whether one model is statistically superior to another based on the elpd_diff.

4. Interpretation

Once the best model is selected, the coach provides tools to answer your actual research questions:

  • Estimated Marginal Means (EMMs): For understanding group differences.
  • Probability of Direction (pd): The probability that an effect is strictly positive or negative.
  • Practical Significance: Checks if the effect size exceeds a specific threshold of practical utility (ROPE).

Reproducibility

The goal of qbrms is not just to fit models, but to teach you how to write the code.

At the end of the workflow, clicking Insert Code will write a complete, reproducible R script into your active document, preserving every decision you made during the interactive session.