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fdaoutlier

Outlier Detection Tools for Functional Data Analysis

Codecov test coverage Lifecycle: experimental CRAN status CRAN downloads Licence 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:

install.packages("fdaoutlier")

or the latest the development version from GitHub with:

devtools::install_github("otsegun/fdaoutlier")

Example

Generate some functional data with magnitude outliers:

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

dim(simdata$data)
#> [1] 100  50

Next apply the msplot of Dai & Genton (2018)

ms <- msplot(simdata$data)

ms$outliers
#> [1]  4  7 17 26 29 55 62 66 76
simdata$true_outliers
#> [1]  4  7 17 55 66

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.