It is crucial to comprehend the computation of robust standard errors in GAM models when dealing with clustered data. Conventional techniques, such as the sandwich package, are effective for GLMs, but the mgcv package needs different strategies. In order to ensure reliable statistical inference, this article examines various solutions, including bootstrapping and cluster-robust variance estimation. Using these methods helps avoid drawing incorrect inferences when examining public health statistics or financial risk models.