https://github.com/otsegun/fdaoutlier

Outlier Detection Tools for Functional Data Analysis
https://github.com/otsegun/fdaoutlier

outlier-detection

Last synced: 3 months ago
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Outlier Detection Tools for Functional Data Analysis

• Host: GitHub
• URL: https://github.com/otsegun/fdaoutlier
• Owner: otsegun
• License: gpl-3.0
• Created: 2019-11-20T21:08:39.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-10-11T12:12:14.000Z (over 2 years ago)
• Last Synced: 2025-10-22T03:54:03.995Z (3 months ago)
• Topics: outlier-detection
• Language: R
• Homepage:
• Size: 1.69 MB
• Stars: 5
• Watchers: 3
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE.md

Awesome Lists containing this project

README

          
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# fdaoutlier

Outlier Detection Tools for Functional Data Analysis

[![Codecov test coverage](https://codecov.io/gh/otsegun/fdaoutlier/branch/master/graph/badge.svg)](https://app.codecov.io/gh/otsegun/fdaoutlier?branch=master&token=0c40801f-206f-4bb4-9b3b-5f493a3130c9)

[![Lifecycle: experimental](https://lifecycle.r-lib.org/articles/figures/lifecycle-experimental.svg)](https://lifecycle.r-lib.org/articles/stages.html)

[![CRAN status](https://www.r-pkg.org/badges/version/fdaoutlier)](https://CRAN.R-project.org/package=fdaoutlier)

[![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/fdaoutlier)](https://cran.r-project.org/package=fdaoutlier)

[![Licence](https://img.shields.io/badge/licence-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html)

`fdaoutlier` is a collection of outlier detection tools for functional data analysis. Methods implemented include directional outlyingness, MS-plot, total variation depth, and sequential transformations among others. 

## Installation

You can install the current version of fdaoutliers from CRAN with:

```{r echo = T, eval = F}

install.packages("fdaoutlier")

```

or the latest the development version from [GitHub](https://github.com/) with:

```{r echo = T, eval = F}

devtools::install_github("otsegun/fdaoutlier")

```

## Example

Generate some functional data with magnitude outliers:

```{r, fig.align='center', fig.width = 10, fig.height=8}

library(fdaoutlier)

simdata <- simulation_model1(plot = T, seed = 1)

dim(simdata$data)

```

Next apply the msplot of Dai & Genton (2018)

```{r, fig.align='center', fig.width = 10, fig.height=8}

ms <- msplot(simdata$data)

ms$outliers

simdata$true_outliers

```

## Methods Implemented

1. MS-Plot (Dai & Genton, 2018)

2. TVDMSS (Huang & Sun, 2019)

3. Extremal depth (Narisetty & Nair, 2016)

4. Extreme rank length depth (Myllymäki et al., 2017; Dai et al., 2020)

5. Directional quantile (Myllymäki et al., 2017; Dai et al., 2020)

6. Fast band depth and modified band depth (Sun et al., 2012)

7. Directional Outlyingness (Dai & Genton, 2019)

8. Sequential transformation (Dai et al., 2020)

## Bugs and Feature Requests

Kindly open an issue using [Github issues](https://github.com/otsegun/fdaoutlier/issues).