Outlier Detection Using Partitioning Clustering Algorithms

You can download a stable version of the package odetector from CRAN cloud as follows:

install.package("odetector", repos="https://cloud.r-project.org")

In order to install the most recent version of the package odetector from the GitHub repository you should first install the devtools package from CRAN into your local system. Then you can install the package odetector using install_github of devtools package as shown with the R code chunks below:

if(!require(devtools)) {install.packages('devtools'); require(devtools)}
devtools::install_github("zcebeci/odetector")

If you would like to have a compiled version of the vignettes of the package try to install the package odetector using install_github with build_vignettes argument set to TRUE as shown below:

if(!require(devtools)) {install.packages('devtools'); require(devtools)}
 devtools::install_github("zcebeci/odetector", build_vignettes=TRUE)

If you have not already installed rmarkdown and prettydoc in your local system, before running the above install commands firstly install these packages as following:

install.packages('prettydoc')

After you installed the package odetector, run the following command in order to load it to R working space.

library(odetector)

To get help about the use of functions included in the package odetector, run help in R as seen in the following code chunk.

help(package="odetector")

For reaching the detailed vignette about the usage of package with examples, run the following command and then click HTML link on the accessed page in your web browser. Read the vignette and apply the examples.

browseVignettes("odetector")

To cite the package please use one of the following items fits to your references list:

Cebeci Z, Cebeci C, Tahtali Y, Bayyurt L. 2022. Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering. PeerJ Computer Science 8:e1060 https://doi.org/10.7717/peerj-cs.1060

or in BibTeX format as follows:

@misc{zcebeciodetector,
  author = {Cebeci, Z. and Cebeci, C. and Tahtali, Y. and Bayyurt, L.},
  title = {Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering},
  year = {2022},
  journal = {PeerJ Computer Science},
  volume={8:e1060},
  howpublished = {\url{https://doi.org/10.7717/peerj-cs.1060}},
}