https://github.com/tbeason/nonparametricregression.jl

Simple local constant and local linear regressions in Julia
https://github.com/tbeason/nonparametricregression.jl

julia kernel-smoothing nonparametric-regression

Last synced: about 2 months ago
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Simple local constant and local linear regressions in Julia

• Host: GitHub
• URL: https://github.com/tbeason/nonparametricregression.jl
• Owner: tbeason
• License: other
• Created: 2021-12-21T21:55:42.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-04-28T19:07:46.000Z (over 1 year ago)
• Last Synced: 2024-10-09T17:24:02.426Z (3 months ago)
• Topics: julia, kernel-smoothing, nonparametric-regression
• Language: Julia
• Homepage:
• Size: 30.3 KB
• Stars: 7
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# NonparametricRegression.jl

![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)

[![build](https://github.com/tbeason/NonparametricRegression.jl/workflows/CI/badge.svg)](https://github.com/tbeason/NonparametricRegression.jl/actions?query=workflow%3ACI)

[![codecov.io](http://codecov.io/github/tbeason/NonparametricRegression.jl/coverage.svg?branch=main)](http://codecov.io/github/tbeason/NonparametricRegression.jl?branch=main)

This package implements non-parametric regression, also called local regression or kernel regression. Currently the functionality is limited to univariate regressions and to only the local constant (`localconstant`) and local linear (`locallinear`,`llalphabeta`) estimators. Automatic bandwidth selection is done by leave-one-out cross validation or by optimizing the bias-corrected AICc statistic.

The two important exported convenience methods are `npregress` and `optimalbandwidth` which abstract from a lot of the implementation detail and allow you to easily switch estimators or bandwidth selection procedures.

## Examples

```julia

using NonparametricRegression

npregress

```

## Detail

- Scaled Gaussian kernel (`GaussianKernel`) by default (aliased by `NormalKernel(h)` where `h` is the bandwidth). Other available kernels are `UniformKernel` and `EpanechnikovKernel`. Adding a new kernel would be a relatively easy PR, see `src/kernels.jl`.

- For local linear estimation, two functions are provided. The first is `locallinear` which explicitly computes a weighted average of `y` as in `localconstant`. The second is `llalphabeta` which computes (and returns) the intercept and slope terms of the local linear regression, the intercept of which is the expected `y`. `llalphabeta` requires only one pass over the data, so is more performant than `locallinear` because computing the weights requires 2 passes, but the results are identical modulo any small numerical epsilons.

- Care was taken to make things non-allocating and performant. The package does not use the "binning" technique that other packages use (R's KernSmooth, for example), so on very large datasets there could be a performance loss relative to those packages. The package does not use multithreading, so again some performance gain could be had here if needed. PRs welcome.

## Related

KernelDensity.jl is a nice package for doing kernel density estimation. 

KernelEstimators.jl is an outdated package which I found after already implementing most of this package. Consider this an updated version I guess.

LOESS.jl is a package implementing a similar but different type of local regression (loess, obviously).