Kernel Density
KernelDensity.jl provides kernel density estimators for Julia, including univariate density estimation.
About Kernel Density
KernelDensity.jl provides kernel density estimators for Julia. It builds a UnivariateKDE from a real vector with kde(data) and a BivariateKDE from a tuple of vectors or a two-column matrix, returning gridded coordinates and density values for plotting.
It supports choice of kernel distribution, bandwidth, boundaries, grid and observation weights, plus bandwidth selection by least-squares cross-validation with kde_lscv. Intermediate values can be queried with pdf via Interpolations.jl. It uses FFTs internally, so boundaries need sufficient spacing to prevent wrap-around and npoints defaults to 2048 with power-of-2 values for optimal efficiency.
Key features
- Univariate density estimation from real vector via kde
- Bivariate density estimation from tuple or two-column matrix
- Bandwidth selection by Silverman's rule or least-squares cross-validation
- Custom kernels from Distributions.jl families
- Observation weights via vectors or StatsBase weights
- Interpolated density queries via pdf and InterpKDE
Best for: Julia users who need univariate or bivariate kernel density estimates for analysis or plotting.
Good for
- Handles both univariate and bivariate data inputs
- Supports custom kernels, grids, boundaries and observation weights
- Allows pdf interpolation of gridded estimates, with InterpKDE for repeated queries
Watch out for
- Requires sufficient boundary spacing to prevent FFT wrap-around
- Non-power-of-2 npoints reduces FFT efficiency
Kernel Density at a glance
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Kernel Density FAQ
How do I create a univariate density estimate?
Use U = kde(data) with a real vector. The result contains gridded coordinates in U.x and the estimate in U.density.
How do I select bandwidth by least-squares cross-validation?
Use kde_lscv(data). It accepts the same keyword arguments as kde, except bandwidth.
How do I get density values between grid points?
Use pdf(k, x) for univariate or pdf(k, x, y) for bivariate. For multiple calls, construct ik = InterpKDE(k) first.
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