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Kernel Density Estimation

KernelDensityEstimation.jl is a package for calculating univariate (1D) kernel density estimates from vectors of data. Its main features (and limitations) are:

• Uses a Gaussian kernel for smoothing (truncated at 4σ).
• Supports closed boundaries.
• Supports processing weighted samples.
• Provides higher-order estimators to better capture variation in width and slope of distributions.
• A more sophisticated bandwidth estimator than the typical Silverman rule.
• Limited to 1D curves — does not support 2D densities.

This package largely implements the algorithms described by Lewis (2019)¹ (and its corresponding Python package, GetDist).

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Footnotes

1. A. Lewis. GetDist: a Python package for analysing Monte Carlo samples (2019), arXiv:1910.13970. ↩