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So, `pseboxmse` brings all these components together to provide a robust estimate of the MSE, especially when your data isn't playing nice (i.e., not normally distributed). The function optimizes the Box-Cox transformation parameter and fits the penalized spline model simultaneously to minimize the estimated MSE. It's particularly useful in scenarios where traditional linear models fall short due to non-linearity or non-normality.
