# GADMA 
[](https://gadma.readthedocs.io/en/latest/?badge=latest) [](https://github.com/ctlab/GADMA/actions) [](https://codecov.io/gh/ctlab/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>
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"description": "# GADMA \n\n[](https://gadma.readthedocs.io/en/latest/?badge=latest) [](https://github.com/ctlab/GADMA/actions) [](https://codecov.io/gh/ctlab/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* [\u2202a\u2202i](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 \u221e 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 \u2014 the most common choice of optimization,\n* Bayesian optimization \u2014 for demographic inference with time-consuming evaluations, e.g. for four and five populations using *moments* or \u2202a\u2202i.\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\u2019Brien, 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>\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, <https://doi.org/10.1093/gigascience/giad059>\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, <https://doi.org/10.1093/g3journal/jkad080>\n",
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