### 2 unstable releases

0.2.0 | Mar 22, 2024 |
---|---|

0.1.0 | Oct 16, 2022 |

#**950** in Algorithms

**37** downloads per month

**MIT**license

185KB

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# kernel-density-estimation

Kernel density estimation in Rust.

Kernel density estimation (KDE) is a non-parametric method to estimate the probability density function of a random variable by taking the summation of kernel functions centered on each data point. This crate serves three major purposes based on this idea:

- Evaluate the probability density function of a random variable.
- Evaluate the cumulative distribution function of a random variable.
- Sample data points from the probability density function.

An excellent technical description of the method is available here.

**Note:** Currently only univariate distributions are supported but multivariate is a goal in the future!

## Examples

**univariate** - This example showcases the core

, `pdf`

, and `cdf`

functionalities for a univariate distribution.`sample`

`cargo`` run`` --`example univariate

**kernel** - This example showcases each of the available kernel functions.

`cargo`` run`` --`example kernel

## Roadmap

Refer to the milestone issues to see the direction the project is headed in future releases or CHANGELOG.md to see the changes between each release.

## License

Distributed under the MIT License. See LICENSE for more information.

## Acknowledgements

- Notes for Nonparametric Statistics[^citation] - An excellent technical description of nonparametric methods referenced heavily in the development of this project.

[^citation]: García-Portugués, E. (2022). Notes for Nonparametric Statistics. Version 6.5.9. ISBN 978-84-09-29537-1.

#### Dependencies

~3MB

~57K SLoC