Metadata-Version: 2.1
Name: gadma
Version: 2.0.2
Summary: A tool for easy-to-use demographic inference
Home-page: https://github.com/ctlab/GADMA
Author: Ekaterina Noskova
Author-email: ekaterina.e.noskova@gmail.com
License: GNU GPLv3+
Project-URL: Documentation, https://gadma.readthedocs.io/en/stable/
Project-URL: Source Code, https://github.com/ctlab/GADMA
Project-URL: Bug Tracker, https://github.com/ctlab/GADMA/issues
Keywords: demographic history,demographic inference,evolution,population genetics
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: Pillow>=4.2.1
Requires-Dist: ruamel.yaml==0.16.12
Requires-Dist: demes
Requires-Dist: demesdraw
Requires-Dist: dadi
Requires-Dist: h5py==3.10.0
Requires-Dist: scikit-allel
Requires-Dist: moments-popgen
Requires-Dist: pandas<=2.2.2

# GADMA ![](http://jb.gg/badges/research-flat-square.svg)

[![Docs](https://readthedocs.org/projects/gadma/badge/?version=latest)](https://gadma.readthedocs.io/en/latest/?badge=latest) [![Build status](https://github.com/ctlab/GADMA/workflows/build/badge.svg)](https://github.com/ctlab/GADMA/actions) [![codecov](https://codecov.io/gh/ctlab/GADMA/branch/master/graph/badge.svg?token=F303UDEWDJ)](https://codecov.io/gh/ctlab/GADMA) [![PyPI - Downloads](https://img.shields.io/pypi/dm/gadma)](https://pypistats.org/packages/gadma)

Welcome to GADMA v2!

GADMA implements methods for automatic inference of the joint demographic history of multiple populations from the genetic data.

**GADMA is a command-line tool**. Basic pipeline presents a series of launches of the global search algorithm followed by the local search optimization.

GADMA provides two types of demographic inference: 1) for user-specified model of demographic history or a custom model, 2) automatic inference for the model with specified structure (up to three populations, see more [here](https://gadma.readthedocs.io/en/latest/user_manual/set_model/set_model_struct.html)).

GADMA provides choice of several engines of demographic inference. This list will be extended in the future. Available engines and maximum number of supported populations for custom model:

* [∂a∂i](https://bitbucket.org/gutenkunstlab/dadi/) (up to 5 populations)
* [*moments*](https://bitbucket.org/simongravel/moments/) (up to 5 populations)
* [*momi2*](https://github.com/popgenmethods/momi2/) (up to ∞ populations)
* [*momentsLD*](https://bitbucket.org/simongravel/moments/) - extenstion of *moments* (up to 5 populations)

More information about engines see [here](https://gadma.readthedocs.io/en/latest/user_manual/set_engine.html).

GADMA features various optimization methods ([global](https://gadma.readthedocs.io/en/latest/api/gadma.optimizers.html#global-optimizers-list) and [local](https://gadma.readthedocs.io/en/latest/api/gadma.optimizers.html#local-optimizers-list) search algorithms) which may be used for [any general optimization problem](https://gadma.readthedocs.io/en/latest/api_examples/optimization_example.html).

Two global search algorithms are supported in GADMA:

* Genetic algorithm — the most common choice of optimization,
* Bayesian optimization — for demographic inference with time-consuming evaluations, e.g. for four and five populations using *moments* or ∂a∂i.

GADMA is developed in Computer Technologies laboratory at ITMO University under the supervision of [Vladimir Ulyantsev](https://ulyantsev.com/) and Pavel Dobrynin. The principal maintainer is [Ekaterina Noskova](http://enoskova.me/) (ekaterina.e.noskova@gmail.com)

**GADMA is now of version 2!** See [Changelog](https://gadma.readthedocs.io/en/latest/changelogs.html).

### Documentation

Please see [documentation](https://gadma.readthedocs.io) for more information including installation instructions, usage, examples and API.

## Getting help

[F.A.Q.](https://gadma.readthedocs.io/en/latest/faq.html)

Please don't be afraid to contact me for different problems and offers via email ekaterina.e.noskova@gmail.com. I will be glad to answer all questions.

Also you are always welcome to [create an issue](https://github.com/ctlab/GADMA/issues) on the GitHub page of GADMA with your question.

## Citations

Please see full list of citations in [documentation](https://gadma.readthedocs.io/en/latest/citations.html).

If you use GADMA in your research please cite:

Ekaterina Noskova, Vladimir Ulyantsev, Klaus-Peter Koepfli, Stephen J O’Brien, Pavel Dobrynin, GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data, *GigaScience*, Volume 9, Issue 3, March 2020, giaa005, <https://doi.org/10.1093/gigascience/giaa005>

If you use GADMA2 in your research please cite:

Ekaterina Noskova, Nikita Abramov, Stanislav Iliutkin, Anton Sidorin, Pavel Dobrynin, and Vladimir Ulyantsev, GADMA2: more efficient and flexible demographic inference from genetic data, *GigaScience*, Volume 12, August 2023, giad059, <https://doi.org/10.1093/gigascience/giad059>

If you use Bayesian optimization please cite:

Ekaterina Noskova and Viacheslav Borovitskiy, Bayesian optimization for demographic inference, *G3 Genes|Genomes|Genetics*, Volume 13, Issue 7, July 2023, jkad080, <https://doi.org/10.1093/g3journal/jkad080>
