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A joint optimization plot, shortly JOP, graphically displays the result of a loss function based robust parameter design for multiple responses. Different importance of reaching a target value can be assigned to the individual responses by weights. The JOP method simultaneously runs through a whole range of possible weights. For each weight matrix a parameter setting is derived which minimizes the estimated expected loss. The joint optimization plot displays these settings together with corresponding expected values and standard deviations of the response variable. The R package JOP provides all tools necessary to apply the JOP approach to a given data set. It also returns parameter settings for a desirable compromise of achieved expected responses chosen from the plot.