Metadata-Version: 2.4
Name: kern_kde
Version: 0.2.3
Summary: kernel density estimation backed by a C extension
Home-page: https://github.com/WojtekGrbs/kern
Author: Wojciech Grabias
License: MIT
Project-URL: Documentation, https://wojtekgrbs.github.io/kern/
Project-URL: Source, https://github.com/WojtekGrbs/kern
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.23
Provides-Extra: docs
Requires-Dist: sphinx>=7; extra == "docs"
Requires-Dist: furo>=2024.1.29; extra == "docs"
Provides-Extra: test
Requires-Dist: pytest>=8; extra == "test"
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Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
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# kern

`kern` is a minimalistic kernel density estimation package backed by optimized C
implementations. It provides exact, approximate, bounded, and multivariate KDE
estimators with a scikit-learn-like API.

## Installation

```console
python -m pip install kern_kde
```

## Example usage
```python
from kern import KernelDensity

model = KernelDensity(bandwidth=0.3).fit([0.0, 0.2, 1.0])
density = model.evaluate([0.1, 0.5])
```

See the [documentation](https://wojtekgrbs.github.io/kern/) for the user guide,
API reference, and source-build options.
