Bootstrapping Clustered Data in R using lmeresampler

Linear mixed-effects models are commonly used to analyze clustered data structures. There numerous packages fit these in R and conduct likelihood-based inference. The implementation of resampling-based procedures for inference more limited. In this paper, we introduce the lmeresampler package bootstrapping nested linear via lme4 or nlme. Bootstrap estimation allows bias correction, adjusted standard errors confidence intervals small samples sizes when distributional assumptions break down. We will also illustrate how bootstrap resampling can be diagnose model class. addition, makes it easy construct interval estimates functions parameters.