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https://github.com/wgunderwood/dyadickde.jl

Dyadic kernel density estimation in Julia
https://github.com/wgunderwood/dyadickde.jl

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Dyadic kernel density estimation in Julia

Host: GitHub
URL: https://github.com/wgunderwood/dyadickde.jl
Owner: WGUNDERWOOD
License: mit
Created: 2022-01-12T16:38:30.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-05-28T21:01:33.000Z (8 months ago)
Last Synced: 2024-05-29T11:43:06.178Z (8 months ago)
Language: Julia
Size: 1.52 MB
Stars: 3
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # DyadicKDE.jl 

Dyadic kernel density estimation in Julia.

[![Build Status](https://github.com/WGUNDERWOOD/DyadicKDE.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/WGUNDERWOOD/DyadicKDE.jl/actions/workflows/CI.yml?query=branch%3Amain)

[![license: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/WGUNDERWOOD/DyadicKDE.jl/blob/main/LICENSE)

[![codecov](https://codecov.io/gh/WGUNDERWOOD/DyadicKDE.jl/branch/main/graph/badge.svg?token=JLN7VK2LT8)](https://codecov.io/gh/WGUNDERWOOD/DyadicKDE.jl)

[![docs stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://WGUNDERWOOD.github.io/DyadicKDE.jl/stable)

[![docs dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://WGUNDERWOOD.github.io/DyadicKDE.jl/dev)

[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)

## Introduction

This repository provides a Julia package which implements the methods for

dyadic kernel density estimation detailed in

[Cattaneo, Feng and Underwood, 2022](https://arxiv.org/abs/2201.05967).

In particular, the package provides the capability for computing

- Point estimates of a univariate dyadic density function

- Pointwise confidence intervals for the density

- Bonferroni-corrected confidence intervals

- Uniform confidence bands

- A rule-of-thumb bandwidth selector

- Counterfactual dyadic density estimation

The currently supported kernels are

- Epanechnikov, order 2

- Epanechnikov, order 4

## Installation

Install from the Julia General registry by starting a

Julia interactive session and running

```julia

] add DyadicKDE

```

Alternatively install from source by running

```julia

] add "https://github.com/WGUNDERWOOD/DyadicKDE.jl.git"

```

The package can then be loaded with

```julia

using DyadicKDE

```

and tested (this may take a few minutes) with

```julia

] test DyadicKDE

```

## Dependencies

DyadicKDE.jl requires

[Julia 1.x](https://docs.julialang.org/en/v1/)

and depends on several other Julia packages listed in

[Project.toml](https://github.com/WGUNDERWOOD/DyadicKDE.jl/tree/main/Project.toml).

## Quick start guide

```julia

# load package

using DyadicKDE

# specify parameters

n_data = 100

kernel_name = "epanechnikov_order_2"

evals = collect(range(-2.0, stop=2.0, length=10))

sdp_solver = "cosmo"

n_resample = 1000

significance_level = 0.05

p = [0.25, 0.0, 0.75]

# make data and get bandwidth

W = make_data(n_data, p)

h_ROT = estimate_ROT_bandwidth(W, "epanechnikov_order_2")

# fit dyadic kernel density estimator

est = DyadicKernelDensityEstimator(

    kernel_name, h_ROT, significance_level,

    n_resample, sdp_solver, evals, W, Dict())

fit(est)

# display properties of estimator

display(est)

# display evaluation points

display(evals')

# display point estimates

display(est.fhat')

# display pointwise confidence intervals

display(est.pci)

# display uniform confidence band

display(est.ucb)

```

## Paper replication

Please refer to the

[replication README](https://github.com/WGUNDERWOOD/DyadicKDE.jl/tree/main/replication/README.md).