Metadata-Version: 2.1
Name: pylfi
Version: 0.0.1
Summary: Likelihood-free inference with Python.
Home-page: https://github.com/nicolossus/neuromodels
Author: Nicolai Haug
Author-email: prof.haug@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: sklearn
Provides-Extra: dev
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: flake8 (>=3.9.2) ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: twine ; extra == 'dev'

# pyLFI

[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
[![Documentation Status](https://readthedocs.org/projects/pylfi/badge/?version=latest)](https://pylfi.readthedocs.io/en/latest/?badge=latest)

pyLFI is a Python toolbox for likelihood-free inference (LFI) for estimating the posterior distributions of model parameters.

This repository provides a Python library for kernel density estimation. In comparison to other Python implementations of kernel density estimation, key features of this library include:

1. Support for weighted samples
2. A variety of kernels, including a smooth, compact kernel.

**Initial development is in progress, and there has not yet been a stable, usable release suitable for the public.**


