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GADMA ![](http://jb.gg/badges/research-flat-square.svg)\n\n[![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)\n\nWelcome to GADMA v2!\n\nGADMA implements methods for automatic inference of the joint demographic history of multiple populations from the genetic data.\n\n**GADMA is a command-line tool**. Basic pipeline presents a series of launches of the global search algorithm followed by the local search optimization.\n\nGADMA 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)).\n\nGADMA 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:\n\n* [∂a∂i](https://bitbucket.org/gutenkunstlab/dadi/) (up to 5 populations)\n* [*moments*](https://bitbucket.org/simongravel/moments/) (up to 5 populations)\n* [*momi2*](https://github.com/popgenmethods/momi2/) (up to ∞ populations)\n* [*momentsLD*](https://bitbucket.org/simongravel/moments/) - extenstion of *moments* (up to 5 populations)\n\nMore information about engines see [here](https://gadma.readthedocs.io/en/latest/user_manual/set_engine.html).\n\nGADMA 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).\n\nTwo global search algorithms are supported in GADMA:\n\n* Genetic algorithm — the most common choice of optimization,\n* Bayesian optimization — for demographic inference with time-consuming evaluations, e.g. for four and five populations using *moments* or ∂a∂i.\n\nGADMA 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)\n\n**GADMA is now of version 2!** See [Changelog](https://gadma.readthedocs.io/en/latest/changelogs.html).\n\n### Documentation\n\nPlease see [documentation](https://gadma.readthedocs.io) for more information including installation instructions, usage, examples and API.\n\n## Getting help\n\n[F.A.Q.](https://gadma.readthedocs.io/en/latest/faq.html)\n\nPlease 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.\n\nAlso you are always welcome to [create an issue](https://github.com/ctlab/GADMA/issues) on the GitHub page of GADMA with your question.\n\n## Citations\n\nPlease see full list of citations in [documentation](https://gadma.readthedocs.io/en/latest/citations.html).\n\nIf you use GADMA in your research please cite:\n\nEkaterina 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, \u003chttps://doi.org/10.1093/gigascience/giaa005\u003e\n\nIf you use GADMA2 in your research please cite:\n\nEkaterina 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, \u003chttps://doi.org/10.1093/gigascience/giad059\u003e\n\nIf you use Bayesian optimization please cite:\n\nEkaterina Noskova and Viacheslav Borovitskiy, Bayesian optimization for demographic inference, *G3 Genes|Genomes|Genetics*, Volume 13, Issue 7, July 2023, jkad080, \u003chttps://doi.org/10.1093/g3journal/jkad080\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fctlab%2Fgadma","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fctlab%2Fgadma","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fctlab%2Fgadma/lists"}