The lme4 and nlme packages have made fitting nested
linear mixed-effects (LME) models quite easy. Using the the
functionality of these packages we can easily use maximum
likelihood or restricted maximum likelihood to fit a
model and conduct inference using our parametric toolkit.
In practice, the assumptions of our model are often violated
to such a degree that leads to biased estimators and
incorrect standard errors. In these situations, resampling
methods such as the bootstrap can be used to obtain consistent
estimators and standard errors for inference.
lmeresampler provides an easy way to bootstrap nested
linear-mixed effects models using either fit using either lme4 or
A variety of bootstrap procedures are available:
the parametric bootstrap:
the residual bootstrap:
the cases (i.e. non-parametric) bootstrap:
the random effects block (REB) bootstrap:
the Wild bootstrap:
In addition to the individual bootstrap functions,
a unified interface to bootstrapping LME models in its
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