bayesDiagnostics provides comprehensive tools for Bayesian model diagnostics and comparison, addressing critical gaps in existing Bayesian diagnostic tools.

• Prior Sensitivity Analysis: Assess posterior robustness to prior specification
• Automated Posterior Predictive Checks: Comprehensive diagnostic batteries
• Advanced Model Comparison: Beyond WAIC/LOO using stacking and cross-validation
• Specialized Convergence Diagnostics: For hierarchical and complex models
• Interactive Prior Elicitation: Tools for expert knowledge integration

Install from GitHub:

devtools::install_github("ikrakib/bayesDiagnostics")

Once available on CRAN:

install.packages("bayesDiagnostics")

library(bayesDiagnostics)
library(brms)

# Fit Bayesian model
fit <- brm(mpg ~ hp + wt, data = mtcars)

# Conduct prior sensitivity analysis
result <- prior_sensitivity(
  model = fit,
  parameters = c("b_hp", "b_wt"),
  prior_grid = list(
    weak = prior(normal(0, 10), class = b),
    strong = prior(normal(0, 1), class = b)
  )
)

print(result)
plot(result)

• prior_sensitivity() - Compare posteriors across priors
• prior_predictive_check() - Validate prior specifications
• prior_robustness() - Test prior hyperparameter sensitivity
• prior_elicitation_helper() - Match expert beliefs to distributions

• automated_ppc() - Comprehensive PPC battery
• ppc_crossvalidation() - K-fold CV with PPCs
• graphical_ppc() - Bayesplot-based visualizations
• bayesian_p_values() - Test statistics for model checking

• model_comparison_suite() - Multi-method comparison
• bayes_factor_comparison() - Bridge sampling for Bayes factors
• predictive_performance() - Out-of-sample assessment

• hierarchical_convergence() - Hierarchical model diagnostics
• effective_sample_size_diagnostics() - ESS assessment

• expert_prior_elicitation() - Interactive prior elicitation
• prior_simulation() - Simulate from prior

For detailed documentation, see the package vignettes:

vignette("bayesDiagnostics")

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

citation("bayesDiagnostics")