Educational Outlier Package with Common Outlier Detection Algorithms.

OutliersLearn R Package

Overview

OutliersLearn is an R package designed to teach and demonstrate different outlier detection algorithms. The algorithms are programmed to provide informative messages while executing on real data, helping users understand the inner workings of each algorithm.

Installation

Will be available to download/install from CRAN To install from GitHub execute this commands in your R session:

install.packages("devtools")
library(devtools)
install_github("MissiegoBeats/OutliersLearn")
library(OutliersLearn)

To install from CRAN:

install.packages("OutliersLearn")
library(OutliersLearn)

In case you want to install the R package using a specific CRAN Mirror:

install.packages("OutliersLearn", repos="<CRAN Mirror URL>")
library(OutliersLearn)

Algorithms included

• Box & whiskers

  boxandwhiskers()

• Standard Deviation Method

  sd_method()

• K neighbors

  knn()

• Local Outlier Factor (Simplified Version)

  lof()

• DBSCAN

  DBSCAN_method()

• Mahalanobis Distance Method

  mahalanobis_method()

Other functions included

• manhattan distance function

  manhattan_dist()

• euclidean distance function

  euclidean_distance()

• quantile function

  quantile_outliersLearn()

• transform to vector function

  transform_to_vector()

• Mean of a vector

  mean_outliersLearn()

• Standard deviation of a vector

  sd_outliersLearn()

• Mahalanobis distance

  mahalanobis_distance()

See more about them using the command help()

Licence

Check the corresponding "LICENSE" file to see the whole license information

Contact me

If there is any question, feel free to open a new issue with the "question" label. If needed, i'll add a Q&A section in the repository issues.