Statistical Models from Xia et al 2024

This repository contains Python code for defining statistical models discussed in the paper. Additionally, it includes Jupyter notebooks that demonstrate how to fit these statistical models to data and plot the main figures related to the model fitting results as presented in the paper.

Repository Structure

• .py files: Contains Python scripts defining statistical models.
• .ipynb files: Includes Jupyter notebooks demonstrating model fitting and figure generation.

Supplementary files

To run the code, you'll need two supplementary files that can be downloaded from the repository in Dryad (https://doi.org/10.5061/dryad.h9w0vt4s0)

• dataset.zip: This file contains the datasets used in the analysis. After unzipping, it will create a folder containing dataset 1 and dataset 2 (see details from Methods in the paper).
• stats_model_fitting_res.zip: This file contains the fitting results of the statistical models. These results can be directly used for plotting figures when running the code in the Jupyter notebooks.

Please unzip these files and place them in the corresponding directories within your local repository.

Usage

1. Clone or download this repository to your local machine.
2. Unzip the supplementary files provided from the journal.
3. Move the unzipped folders into the same folder as files from the repository. Use the Jupyter notebooks to fit the statistical models to the data, and generate figures.

If you have any questions, feel free to open an issue or contact the corresponding author.