The DDoutlier package provides users with a wide variety of distance- and density-based outlier detection functions. Distance- and density based outlier detection works with local outliers in a multidimensional domain, meaning observations are compared to their respective neighborhood. The algorithms mainly have an advantage within two domains:


All functions require a dataset as input and have a varying number of input parameters influencing the outlier score output. The most common input parameter is the k parameter for constructing the k-nearest neighborhood. To speed up kNN search, the kNN function in the dbscan package is used to construct a kd-tree. For the functions COF, LOCI and LDOF a complete distance matrix is required, leaving out the possibility of using a kd-tree. For the functions RDOS, INFLO and NOF computation of a reverse neighborhood is required, also making it computational heavy.

Removing duplicates and standardizing data is recommended before computing outlier scores.


To install latest version in R use following commands:


Work is currently carried out to make it available in the CRAN repository


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