Kernel Density
A Julia package providing kernel density estimation (KDE) for univariate and bivariate data with flexible bandwidth selection and interpolation.
What is Kernel Density?
KernelDensity.jl is a Julia package that implements kernel density estimation (KDE), a technique for estimating the probability density function of a dataset non-parametrically. It provides tools to smooth and visualize data distributions, supporting both univariate and bivariate data with configurable kernels, bandwidths, and interpolation. The package solves the problem of deriving continuous density estimates from discrete samples, which is fundamental for statistical analysis and plotting.
Data scientists, statisticians, and researchers using Julia for statistical computing, data exploration, or visualization who need robust density estimation methods.
Developers choose KernelDensity.jl for its seamless integration with Julia's statistical ecosystem (e.g., Distributions.jl), efficient FFT-based implementation, and flexibility in bandwidth selection and kernel customization, all within a high-performance, open-source framework.
Overview
Kernel density estimators for Julia
Use Cases
Best For
- Estimating smooth probability densities from sampled data in Julia
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