Torchtree: flexible phylogenetic model development and inference using PyTorch

Bayesian inference has predominantly relied on the Markov chain Monte Carlo (MCMC) algorithm for many years. However, MCMC is computationally laborious, especially for complex phylogenetic models of time trees. This bottleneck has led to the search for alternatives, such as variational Bayes, which can scale better to large datasets. In this paper, we introduce torchtree, a framework written in Python that allows developers to easily implement rich phylogenetic models and algorithms using a fixed tree topology. One can either use automatic differentiation, or leverage torchtree's plug-in system to compute gradients analytically for model components for which automatic differentiation is slow. We demonstrate that the torchtree variational inference framework performs similarly to BEAST in terms of speed and approximation accuracy. Furthermore, we explore the use of the forward KL divergence as an optimizing criterion for variational inference, which can handle discontinuous and non-diffe..., , , # torchtree: flexible phylogenetic model development and inference using PyTorch

Mathieu Fourment, Matthew Macaulay, Christiaan J Swanepoel, Xiang Ji, Marc A Suchard, Frederick A Matsen IV. [torchtree: flexible phylogenetic model development and inference using PyTorch](https://arxiv.org/abs/2406.18044). arXiv:2406.18044 (2024)

## Description of the data

The SI.pdf file contains supplementary methods and figures referenced in the main manuscript (found on Zenodo under Supplemental Information).

The data.zip contains input files and phylogenetic trees used for analyses in the associated manuscript. The data are organized by dataset (`HCV` and `SC2`) and by tool (`beast` and `torchtree`), and include sequence alignments (see next section for SC2 alignment) and configuration files (xml and json files). torchtree uses variational Bayes while BEAST uses MCMC.

```
data/
├── HCV/
│ ├── HCV.fasta # Sequence alignment for HCV
│ ├── HCV.tree # Newick ...,