https://github.com/nignatiadis/smoothingsplines.jl

Cubic smoothing splines in Julia.
https://github.com/nignatiadis/smoothingsplines.jl

julia nonparametric-regression

Last synced: 24 days ago
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Cubic smoothing splines in Julia.

• Host: GitHub
• URL: https://github.com/nignatiadis/smoothingsplines.jl
• Owner: nignatiadis
• License: other
• Created: 2016-05-31T12:21:28.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-07-29T16:48:10.000Z (over 1 year ago)
• Last Synced: 2024-12-10T04:16:30.420Z (25 days ago)
• Topics: julia, nonparametric-regression
• Language: Julia
• Homepage:
• Size: 35.2 KB
• Stars: 34
• Watchers: 8
• Forks: 14
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# SmoothingSplines

[![Build Status](https://github.com/nignatiadis/SmoothingSplines.jl/workflows/CI/badge.svg)](https://github.com/nignatiadis/SmoothingSplines.jl/actions)

[![Coverage](https://codecov.io/gh/nignatiadis/SmoothingSplines.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/nignatiadis/SmoothingSplines.jl)

A Julia package for nonparametric regression with Cubic Smoothing Splines. The initial aim is to provide the same functionality as R's `smooth.spline` function and competitive computational performance. The implementation however is completely independent of the R function and based on the algorithm by Reinsch [1], as described in Chapter 2 of [2].

```julia

using SmoothingSplines

using RDatasets

using Gadfly

cars = dataset("datasets","cars")

X = map(Float64,convert(Array,cars[!,:Speed]))

Y = map(Float64,convert(Array,cars[!,:Dist]))

spl = fit(SmoothingSpline, X, Y, 250.0) # λ=250.0

Ypred = predict(spl) # fitted vector

plot(layer(x=X, y=Y, Geom.point),

	layer(x=X, y=Ypred, Geom.line, 	Theme(default_color=colorant"red")))

predict(spl, 20.0) #prediction at arbitrary point

```

### TODO

* Better docs

* conversion between regularization parameter λ and degrees of freedom

* automatic selection of λ (LOOCV, GCV)

* subsampling of design grid for higher efficiency

**References**

[1] Reinsch, Christian H. "Smoothing by spline functions." Numerische mathematik 10.3 (1967): 177-183.

[2] Green, Peter J., and Bernard W. Silverman. Nonparametric regression and generalized linear models: a roughness penalty approach. CRC Press, 1993.