# estimagic 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. For a complete introduction to optimization in estimagic, check out the {ref}`estimagic_scipy2022` ```{raw} html
``` ```{toctree} --- hidden: true maxdepth: 1 --- getting_started/index how_to_guides/index explanations/index reference_guides/index ``` ## Highlights ### Optimization - estimagic wraps algorithms from *scipy.optimize*, *nlopt*, *pygmo* and more. See {ref}`list_of_algorithms` - estimagic implements constraints efficiently via reparametrization, so you can solve constrained problems with any optimzer that supports bounds. See {ref}`constraints` - The parameters of an optimization problem can be arbitrary pytrees. See {ref}`params`. - The complete history of parameters and function evaluations can be saved in a database for maximum reproducibility. See [How to use logging] - Painless and efficient multistart optimization. See [How to do multistart] - The progress of the optimization is displayed in real time via an interactive dashboard. See {ref}`dashboard`. > ```{image} _static/images/dashboard.gif > --- > align: center > scale: 80 % > --- > ``` ### 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. See [MSM Tutorial] - 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. **Useful links for search:** {ref}`genindex` | {ref}`modindex` | {ref}`search` ```{toctree} --- hidden: true maxdepth: 1 --- development/index algorithms ``` [how to do multistart]: how_to_guides/optimization/how_to_do_multistart_optimizations [how to use logging]: how_to_guides/optimization/how_to_use_logging [msm tutorial]: getting_started/estimation/first_msm_estimation_with_estimagic