Automatic outlier detection in R Our model processes millions of multivariate observations; manual outlier detection is impractical. I am looking for a method of automatic outlier detection. 
I have been trying to use R package mvoutliers, especially function pcout, and get the error 

More than 50% equal values in one or more variables!

The problem is that our data is quite sparse and many variables include More than 50% equal values.


*

*Is there any way around (some data preprocessing) that would still allow me to use pcout?

*Is there another recommended R package/function/method for automatic outlier detection?

 A: In my experience, the term outliers doesn't make sense without the context of the application. That is, if you want to exclude data points from your data set, you should be able to give reasons why this or that data point is removed. These reasons may suggest appropriate filtering rules. 
Therefore I think that something like the "recommended R package/function/method for automatic outlier detection" cannot exist in general, it can exist only for particular types of data/applications.
A: An outlier is only an outlier with respect to some model. What's hugely discrepant under one set of assumptions is just an ordinary point under another, something to be expected in the ordinary course of events.
If your data are often of a particular form, it might help to think about what you mean by 'outlier' for that kind of distributional situation, and tailor your automated procedure to deal with the situation you're in, rather than try to squeeze it into something it's not suited to.
A: Try adding a very samll random number to your data.
