Local Regression
A pure Julia implementation of LOESS (locally weighted regression) for smoothing scatterplots and fitting non-parametric models.
What is Local Regression?
Loess.jl is a Julia package that implements LOESS (locally weighted regression), a non-parametric method for fitting smooth curves to scatterplot data. It provides tools for smoothing noisy data and visualizing trends without assuming a specific functional form. The package is based on the fast kd-tree approximation algorithm described in Cleveland's original papers.
Data scientists, statisticians, and researchers working in Julia who need to perform local regression analysis or smooth scatterplot data for visualization and modeling.
Loess.jl offers a pure Julia implementation with no external dependencies, providing performance and ease of use while staying faithful to the widely-used netlib LOESS algorithm. It integrates well with Julia's plotting ecosystem for seamless visualization.
Overview
Local regression, so smooooth!
Use Cases
Best For
- Smoothing noisy scatterplot data to reveal underlying trends
- Performing non-parametric regression when the functional form is unknown
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