Crate 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 here¹.

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1. García-Portugués, E. (2022). Notes for Nonparametric Statistics. Version 6.5.9. ISBN 978-84-09-29537-1. ↩

Modules

• bandwidth
  Bandwidth selection strategies.
• float
  Float constraints for generic math.
• kde
• kernel
  Kernel functions.
• prelude
  use kernel_density_estimation::prelude::*; to import all common functionality.