This package requires the model at hand is in form of y=X\beta + \epsilon and the estimation is based on least squares or maximum likelihood estimation. Implemented methods take the model as a @formula object or X and y where X is the design matrix and y is the response vector. As I know and correct me if I am wrong, the main estimation process in mixed models is generally handled by EM algorithm. I have never researched the literature on this topic, but there may be other outlier detection algorithms for such models.
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