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The major shortcoming of robust methodology in statistical linear models has been a limitation primarily to parameter estimation in fixed effects models. The major competitor, classical least squares methodology, by contrast offers a unified treatment of estimation, testing, and multiple comparisons techniques in a wide range of fixed and random effects models. Research efforts have been directed toward filling the void in robust methods in order to provide a complete alternative to least squares procedures. This report summarizes the results. (Author)