PopFunc: Bayesian Model-Averaging of Parametric Coalescent Models

PopFunc is a BEAST2 package that enables Bayesian inference of parametric population-growth models under a flexible model-averaging framework. By using discrete indicator variables, PopFunc allows users to explore multiple candidate demographic models in a single run without prior commitment to a single growth function.

Documentation

1. Introduction

   • High-level overview of PopFunc’s motivation, theoretical background, and typical use cases.

2. Installation & Requirements

   • Detailed setup instructions, prerequisites, and compatibility notes.

3. Bayesian Model Averaging (BMA) Setup & Basic Usage

   • How to configure model indicators, enable multiple parametric growth models, and interpret outputs.

4. Examples & Tutorials

   • Step-by-step demonstrations, scripts, and recommended workflows for applying PopFunc to real or simulated data.

5. References

   • Cited works, recommended reading, and relevant publications for further study.

If you have any questions or need further help, feel free to check out the BEAST Users Google Group.

Enjoy exploring PopFunc!