kerneldensityestimation
Kernel density estimation in Rust.
Kernel density estimation (KDE) is a nonparametric 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
, cdf
, and sample
functionalities for a univariate distribution.
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^{1}  An excellent technical description of nonparametric methods referenced heavily in the development of this project.
Footnotes

GarcíaPortugués, E. (2022). Notes for Nonparametric Statistics. Version 6.5.9. ISBN 9788409295371.
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