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bayesma 0.0.0.9000

- Model Basics
- One-Stage vs Two-Stage Models
- Common-Effect Model
- Random-Effects Models
- Gaussian RE (default)
- Alternative RE Distributions
- Assessment of RE Distributions
- Small-Sample Adjustments
- Multiarm Models
- Model Comparison & Diagnostics
- Advanced Models
- Multivariate Meta-Analysis
- Longitudinal Meta-Analysis
- Meta-Regression
- Custom Models
- Small-Sample & Publication Bias
- Assessment — Bayesian Egger's Test
- Assessment — Funnel Plots
- PET-PEESE
- Selection Weight Models
- BC-BNP Model (Verde & Rosner 2025)
- Mixture Model (Maier)
- Heterogeneity
- RE Mixture Models
- Robust Outlier Mixture
- Other Bias
- INSPECT-SR Assessment
- Risk of Bias Assessment
- Bias-Adjusted Models (Jung & Aloe 2026)
- Bias-Adjusted Models (Verde 2021)
- Overview & Conventions
- Common-Effect Model
- Random-Effects Models
- Gaussian RE
- Student-t RE
- Skew-Normal RE
- Mixture RE
- Small-Sample Adjustments
- Multiarm Model
- Multivariate Model
- Longitudinal Model
- Meta-Regression
- Publication Bias Models
- PET-PEESE
- Vevea-Hedges Selection Model
- Copas Selection Model
- BC-BNP Model
- Mixture Model (Maier)
- Heterogeneity Models
- RE Mixture Model
- Robust Outlier Mixture
- Bias-Adjusted Models
- Verde (2021)
- Jung & Aloe (2026)

Print Stan code from a fitted model

Source: R/bayesma_helpers.R

stan_code.Rd

Print Stan code from a fitted model

Usage

stan_code(fit)

Arguments

fit: A CmdStanFit object.

Value

The Stan code string (invisibly).

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Developed by Benjamin Moran, Thomas Payne.

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