https://github.com/elkronos/bmber

Bayesian Model Building and Evaluation Repository
https://github.com/elkronos/bmber

bayesian-inference bayesian-statistics r rstan sensitivity-analysis statistics

Last synced: about 1 year ago
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Bayesian Model Building and Evaluation Repository

• Host: GitHub
• URL: https://github.com/elkronos/bmber
• Owner: elkronos
• Created: 2023-08-11T23:14:55.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2025-02-18T04:30:51.000Z (over 1 year ago)
• Last Synced: 2025-02-18T05:26:46.983Z (over 1 year ago)
• Topics: bayesian-inference, bayesian-statistics, r, rstan, sensitivity-analysis, statistics
• Language: R
• Homepage:
• Size: 26.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# bmbeR: Bayesian Model Building and Evaluation Repository

This repository contains a set of R scripts designed to build, evaluate, visualize, and perform sensitivity analysis on Bayesian models. These scripts use a mixture of `rstan`, `rstanarm`, and other Bayesian analysis libraries to facilitate the modeling process.

## Scripts and Their Functions:

### libraries_and_setup.R

- **Purpose:** Loads the necessary libraries and global settings for the entire modeling workflow.

- **Libraries Used:**

  - `rstanarm`

  - `bayesplot`

  - `ggplot2`

  - `cowplot`

  - `purrr`

  - `rstan`

  - `loo`

### distributions.R

- **Purpose:** Defines a comprehensive set of prior distribution generators.

- **Functions:**

  - `student_t_prior`

  - `normal_prior`

  - `cauchy_prior`

  - `uniform_prior`

  - `beta_prior`

  - `gamma_prior`

  - `binomial_prior`

  - `poisson_prior`

  - `lognormal_prior`

  - `bernoulli_prior`

- **Global Objects and Utilities:**

  - `original_distributions`, `distributions`

  - `add_distribution`, `reset_distributions`, `get_prior_distribution`

### utilities.R

- **Purpose:** Provides utility functions used across the repository.

- **Functions:**

  - `validate_positive_integer`

  - `validate_numeric`

  - `%||%` (null-coalescing operator)

### empirical_bayes.R

- **Purpose:** Computes empirical Bayesian hyperparameter specifications from data.

- **Functions:**

  - `empirical_bayes_priors`: Fits a linear model and maps coefficient estimates and standard errors to hyperparameters for various prior distributions.

### model_convergence.R

- **Purpose:** Checks convergence diagnostics of a fitted Bayesian model.

- **Functions:**

  - `check_convergence`: Assesses if a model has converged based on Rhat and effective sample size (ESS) metrics, and optionally generates trace plots.

### model_fitting.R

- **Purpose:** Fits Bayesian models using specified prior configurations and data.

- **Functions:**

  - `build_stanarm_priors`: Converts hyperparameter lists into rstanarm prior objects.

  - `fit_model_with_prior`: Fits a Bayesian model using `rstanarm::stan_glm` with given priors, and checks for convergence.

### model_visualization.R

- **Purpose:** Generates diagnostic and posterior visualization plots for fitted models.

- **Functions:**

  - `generate_plot`: Generates diagnostic plots (trace, histogram, density, and autocorrelation) for a model.

  - `plot_posterior_distributions`: Visualizes posterior distributions with 95% credible intervals using bayesplot.

### model_sensitivity.R

- **Purpose:** Performs sensitivity analysis across different prior configurations.

- **Functions:**

  - `sensitivity_analysis`: Iterates through a list of prior configurations, fits models, and computes performance metrics (using LOO) to assess how changes in priors affect model outcomes.

### model_evaluation.R

- **Purpose:** Evaluates the predictive performance of fitted models.

- **Functions:**

  - `evaluate_model_performance`: Computes performance metrics such as RMSE and MAE for regression tasks, or accuracy, precision, recall, and F1 Score for classification tasks.