Local Regression
Loess.jl is a Julia implementation of local regression using a fast kd-tree-based approximation.
About Local Regression
Loess.jl is a pure Julia implementation of local regression, using a fast kd-tree-based approximation derived from the methodology in Cleveland et al's original loess papers and the netlib loess C/Fortran code that underpins tools like R's loess function. The package provides two core functions, loess for training local regression models and predict for applying trained models, with smoothing primarily controlled by the span keyword argument. Multivariate regression support is not yet fully implemented, though most required components are already present.
Key features
- Pure Julia local regression implementation
- Fast kd-tree-based approximation for performance
- loess function for model training
- predict function for model application
- Smoothing controlled via span keyword argument
- Compatible with Julia plotting libraries like Plots and Gadfly
Best for: Julia developers and data analysts performing local regression for smoothing and local fitting tasks.
Good for
- Pure Julia implementation for native Julia workflows
- Uses fast kd-tree-based approximation for efficient computation
- Based on well-established loess methodology used in R and other tools
Watch out for
- Multivariate regression support is incomplete
Local Regression 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 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Guides for Developer Tools
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Local Regression FAQ
What core functions does Loess.jl provide?
It exports loess for training models and predict for applying trained models.
How is smoothing adjusted in Loess.jl models?
Smoothing is primarily controlled via the span keyword argument.
Is multivariate regression supported in Loess.jl?
Multivariate regression is not yet fully implemented, though most components are already in place.
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