physher
is a program for estimating evolutionary rates and divergence times from genetic, amino acid, codon, and generic data.
The current version of physher is incompatible with the first version. Documentation for installing physher1 can be found here and the manual is located here.
A C compiler such as gcc
or clang
is required. It is also requires the GSL library.
On Debian-based systems, dependencies can be installed via apt
:
sudo apt install gcc gsl
On MacOS, dependencies can be installed using a package manager such as Homebrew.
brew install llvm gsl
Other package managers such as conda and MacPorts can also be used to install dependencies.
To build physher
from source you can run
git clone https://github.com/4ment/physher
cmake -S physher/ -B physher/build
cmake --build physher/build/ --target install
If the installation was successful, this command should print the version of physher
physher
A subset of physher's functionalities is exposed in C++ wrappers. These wrappers are used in torchtree, a python program, through bindings and torchtree-physher.
A C++ compiler such as g++ or clang++ is required. Compilers can be installed using apt
or homebrew
git clone https://github.com/4ment/physher
cmake -S physher/ -B physher/build -DBUILD_CPP_WRAPPER=on
cmake --build physher/build/ --target install
cmake -S physher/ -B physher/build -DBUILD_TESTING=on
cmake --build physher/build/ --target install
ctest --test-dir physher/build/
cd examples/fluA
physher JC69-time-ELBO.json
Some examples of projects using physher
- marginal-experiments: Evaluation of 19 dubious ways to compute marginal likelihood estimates. 10.1093/sysbio/syz046.
- phylostan: Comparison of phylostan and
physher
using variational inference. 10.1101/702944. - gradient-benchmark: Benchmarking of automatic differentiation and analical gradients. 10.1093/gbe/evad099
- torchtree-physher: Plugin provinding fast calculation of phylogenetic functions in
physher
to torchtree.
Distributed under the GPLv2 License. See LICENSE for more information.
Fourment M and Holmes EC. Novel non-parametric models to estimate evolutionary rates and divergence times from heterochronous sequence data. BMC Evolutionary Biology, 2014. doi: 10.1186/s12862-014-0163-6