# estimagic
[](https://pypi.org/project/estimagic)
[](https://pypi.org/project/estimagic)
[](https://anaconda.org/conda-forge/estimagic)
[](https://anaconda.org/conda-forge/estimagic)
[](https://pypi.org/project/estimagic)
[](https://estimagic.readthedocs.io/en/latest)
[](https://github.com/OpenSourceEconomics/estimagic/actions?query=branch%3Amain)
[](https://codecov.io/gh/OpenSourceEconomics/estimagic)
[](https://github.com/OpenSourceEconomics/estimagic/actions?query=branch%3Amain)
[](https://github.com/psf/black)
[](CODE_OF_CONDUCT.md)
[](https://pepy.tech/project/estimagic)
## Introduction
estimagic is a Python package for nonlinear optimization with or without constraints. It
is particularly suited to solve difficult nonlinear estimation problems. On top, it
provides functionality to perform statistical inference on estimated parameters.
### Optimization
- estimagic wraps algorithms from *scipy.optimize*, *nlopt*, *pygmo* and more.
- estimagic implements constraints efficiently via reparametrization, so you can solve
constrained problems with any optimzer that supports bounds.
- The parameters of an optimization problem can be arbitrary pytrees
- The complete history of parameters and function evaluations can be saved in a database
for maximum reproducibility.
- Painless and efficient multistart optimization.
- The progress of the optimization is displayed in real time via an interactive
dashboard.
<img src="docs/source/_static/images/dashboard.gif">
### Estimation and Inference
- You can estimate a model using method of simulated moments (MSM), calculate standard
errors and do sensitivity analysis with just one function call.
- Asymptotic standard errors for maximum likelihood estimation.
- estimagic also provides bootstrap confidence intervals and standard errors. Of course
the bootstrap procedures are parallelized.
### Numerical differentiation
- estimagic can calculate precise numerical derivatives using
[Richardson extrapolations](https://en.wikipedia.org/wiki/Richardson_extrapolation).
- Function evaluations needed for numerical derivatives can be done in parallel with
pre-implemented or user provided batch evaluators.
## Installation
The package can be installed via conda. To do so, type the following commands in a
terminal:
```bash
$ conda config --add channels conda-forge
$ conda install estimagic
```
The first line adds conda-forge to your conda channels. This is necessary for conda to
find all dependencies of estimagic. The second line installs estimagic and its
dependencies.
## Installing optional dependencies
Only `scipy` is a mandatory dependency of estimagic. Other algorithms become available
if you install more packages. We make this optional because most of the time you will
use at least one additional package, but only very rarely will you need all of them.
For an overview of all optimizers and the packages you need to install to enable them
see {ref}`list_of_algorithms`.
To enable all algorithms at once, do the following:
`conda install nlopt`
`pip install Py-BOBYQA`
`pip install DFO-LS`
`conda install petsc4py` (Not available on Windows)
`conda install cyipopt`
`conda install pygmo`
`pip install fides>=0.7.4 (Make sure you have at least 0.7.1)`
## Documentation
The documentation is hosted ([on rtd](https://estimagic.readthedocs.io/en/latest/#))
## Citation
If you use Estimagic for your research, please do not forget to cite it.
```
@Unpublished{Gabler2022,
Title = {A Python Tool for the Estimation of large scale scientific models.},
Author = {Janos Gabler},
Year = {2022},
Url = {https://github.com/OpenSourceEconomics/estimagic}
}
```
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"description": "# estimagic\n\n[](https://pypi.org/project/estimagic)\n[](https://pypi.org/project/estimagic)\n[](https://anaconda.org/conda-forge/estimagic)\n[](https://anaconda.org/conda-forge/estimagic)\n[](https://pypi.org/project/estimagic)\n[](https://estimagic.readthedocs.io/en/latest)\n[](https://github.com/OpenSourceEconomics/estimagic/actions?query=branch%3Amain)\n[](https://codecov.io/gh/OpenSourceEconomics/estimagic)\n[](https://github.com/OpenSourceEconomics/estimagic/actions?query=branch%3Amain)\n[](https://github.com/psf/black)\n[](CODE_OF_CONDUCT.md)\n[](https://pepy.tech/project/estimagic)\n\n## Introduction\n\nestimagic is a Python package for nonlinear optimization with or without constraints. It\nis particularly suited to solve difficult nonlinear estimation problems. On top, it\nprovides functionality to perform statistical inference on estimated parameters.\n\n### Optimization\n\n- estimagic wraps algorithms from *scipy.optimize*, *nlopt*, *pygmo* and more.\n- estimagic implements constraints efficiently via reparametrization, so you can solve\n constrained problems with any optimzer that supports bounds.\n- The parameters of an optimization problem can be arbitrary pytrees\n- The complete history of parameters and function evaluations can be saved in a database\n for maximum reproducibility.\n- Painless and efficient multistart optimization.\n- The progress of the optimization is displayed in real time via an interactive\n dashboard.\n\n<img src=\"docs/source/_static/images/dashboard.gif\">\n\n### Estimation and Inference\n\n- You can estimate a model using method of simulated moments (MSM), calculate standard\n errors and do sensitivity analysis with just one function call.\n- Asymptotic standard errors for maximum likelihood estimation.\n- estimagic also provides bootstrap confidence intervals and standard errors. Of course\n the bootstrap procedures are parallelized.\n\n### Numerical differentiation\n\n- estimagic can calculate precise numerical derivatives using\n [Richardson extrapolations](https://en.wikipedia.org/wiki/Richardson_extrapolation).\n- Function evaluations needed for numerical derivatives can be done in parallel with\n pre-implemented or user provided batch evaluators.\n\n## Installation\n\nThe package can be installed via conda. To do so, type the following commands in a\nterminal:\n\n```bash\n$ conda config --add channels conda-forge\n$ conda install estimagic\n```\n\nThe first line adds conda-forge to your conda channels. This is necessary for conda to\nfind all dependencies of estimagic. The second line installs estimagic and its\ndependencies.\n\n## Installing optional dependencies\n\nOnly `scipy` is a mandatory dependency of estimagic. Other algorithms become available\nif you install more packages. We make this optional because most of the time you will\nuse at least one additional package, but only very rarely will you need all of them.\n\nFor an overview of all optimizers and the packages you need to install to enable them\nsee {ref}`list_of_algorithms`.\n\nTo enable all algorithms at once, do the following:\n\n`conda install nlopt`\n\n`pip install Py-BOBYQA`\n\n`pip install DFO-LS`\n\n`conda install petsc4py` (Not available on Windows)\n\n`conda install cyipopt`\n\n`conda install pygmo`\n\n`pip install fides>=0.7.4 (Make sure you have at least 0.7.1)`\n\n## Documentation\n\nThe documentation is hosted ([on rtd](https://estimagic.readthedocs.io/en/latest/#))\n\n## Citation\n\nIf you use Estimagic for your research, please do not forget to cite it.\n\n```\n@Unpublished{Gabler2022,\n Title = {A Python Tool for the Estimation of large scale scientific models.},\n Author = {Janos Gabler},\n Year = {2022},\n Url = {https://github.com/OpenSourceEconomics/estimagic}\n}\n```\n",
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