https://github.com/seatonullberg/kernel-density-estimation

Kernel density estimation in Rust.
https://github.com/seatonullberg/kernel-density-estimation

kernel-density-estimation probability-density statistics

Last synced: about 1 month ago
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Kernel density estimation in Rust.

• Host: GitHub
• URL: https://github.com/seatonullberg/kernel-density-estimation
• Owner: seatonullberg
• License: mit
• Created: 2022-10-07T18:20:29.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-22T20:51:48.000Z (9 months ago)
• Last Synced: 2024-11-01T21:42:21.725Z (about 2 months ago)
• Topics: kernel-density-estimation, probability-density, statistics
• Language: Rust
• Homepage: https://crates.io/crates/kernel-density-estimation
• Size: 232 KB
• Stars: 26
• Watchers: 1
• Forks: 6
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# kernel-density-estimation

![Crates.io](https://img.shields.io/crates/d/kernel-density-estimation)

![Crates.io](https://img.shields.io/crates/l/kernel-density-estimation)

![Crates.io](https://img.shields.io/crates/v/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:

1) Evaluate the probability density function of a random variable.

2) Evaluate the cumulative distribution function of a random variable.

3) Sample data points from the probability density function.

An excellent technical description of the method is available

[here](https://bookdown.org/egarpor/NP-UC3M/kde-i.html).

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

## Examples

__[univariate](examples/univariate.rs)__ - This example showcases the core `pdf`, `cdf`, and `sample` functionalities for a univariate distribution.

```

cargo run --example univariate

```

![Univariate Distribution](assets/univariate.png)

__[kernel](examples/kernel.rs)__ - This example showcases each of the available kernel functions.

```

cargo run --example kernel

```

![Kernel Functions](assets/kernel.png)

## Roadmap

Refer to the [milestone issues](https://github.com/seatonullberg/kernel-density-estimation/issues) to see the direction the project is headed in future releases or [CHANGELOG.md](./CHANGELOG.md) to see the changes between each release.

## License

Distributed under the MIT License. See [LICENSE](./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.