Metadata-Version: 2.1
Name: numba-stats
Version: 0.8.0
Summary: Numba-accelerated implementations of common probability distributions
Home-page: UNKNOWN
Author: Hans Dembinski
Author-email: hans.dembinski@gmail.com
License: "MIT"
Project-URL: Bug Tracker, https://github.com/hdembinski/numba-stats/issues
Description: # numba-stats
        
        ![](https://img.shields.io/pypi/v/numba-stats.svg)
        
        We provide numba-accelerated implementations of statistical functions for common probability distributions
        
        * Uniform
        * Normal
        * Poisson
        * Exponential
        * Student's t
        * Voigtian
        * Crystal Ball
        * Tsallis
        * Bernstein density (not normalised to unity, use this in extended likelihood fits)
        
        with more to come. The speed gains are huge, up to a factor of 100 compared to `scipy`. Benchmarks are included in the repository and are run by `pytest`.
        
        **You can help with adding more distributions, patches are very welcome.** Implementing a probability distribution is easy. You need to write it in simple Python that `numba` can understand. Special functions from `scipy.special` can be used after some wrapping, see submodule `numba_stats._special.py` how it is done.
        
        Because of limited manpower, this project is barely documented. The documentation is basically `pydoc numba_stats`. The calling conventions are the same as for the corresponding functions in `scipy.stats`. These are sometimes a bit unusual, for example, for the exponential distribution, see the `scipy` docs for details.
        
        # Plans for version 1.0
        
        Version v1.0 (not there yet) will introduce breaking changes to the API.
        ```
        # before v0.8
        from numba_stats import norm_pdf
        from numba_stats.stats import norm_cdf
        
        dp = norm_pdf(1, 2, 3)
        p = norm_cdf(1, 2, 3)
        
        # recommended since v0.8
        from numba_stats import norm
        
        dp = norm.pdf(1, 2, 3)
        p = norm.cdf(1, 2, 3)
        ```
        This change is not only cosmetics, it was necessary to battle the increasing startup times of `numba-stats`. Now you only pay the compilation cost for the distribution that you actually need. The `stats` submodule has been removed. To keep old code running, please pin your numba_stats to version `0.7`.
        
        ## numba-stats and numba-scipy
        
        [numba-scipy](https://github.com/numba/numba-scipy) is the official package and repository for fast numba-accelerated scipy functions, are we reinventing the wheel?
        
        Ideally, the functionality in this package should be in `numba-scipy` and we hope that eventually this will be case. In this package, we don't offer overloads for scipy functions and classes like `numba-scipy` does. This simplifies the implementation dramatically. `numba-stats` is intended as a temporary solution until fast statistical functions are included in `numba-scipy`. `numba-stats` currently does not depend on `numba-scipy`, only on `numba` and `scipy`.
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
