An outlier discovering package for Julia.
At this stage, a naive outlier discovering for numerical vectors is implemented, using the BoxPlot Rules.
This package will be extended for more functionalities in the future.
This method works with 1 dimensional numerical vectors. Suppose we have a random vector of float numbers, then manually append a relatively big number at the end, and we want to find out the outlier(s) among such vector. This can be done using outliers function as following:
using Outliers
a = [randn(10), 100.] # Generate 10 random float numbers, and append a relatively large number as "outlier"
o, idc = outliers(a) # returns the outliers, and according indices in vector