The code for all of the apps can be found on github.
The lme4 and nlme packages have made fitting nested linear mixed-effects models in R quite easy. Using the the functionality of these packages we can easily use maximum likelihood or restricted maximum likelihood to fit our 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 of the bias and standard errors for inference. lmeresampler provides an easy way to bootstrap nested linear-mixed effects models using either the parametric, residual, cases, CGR (semi-parametric), or random effects block (REB) bootstrap, for models fit using either lme4 or nlme. The output from lmeresampler is compatible with the boot package.
The stable release of lmeresampler is available on CRAN.
lmeresampler is still under development. You can watch the development of the lmeresampler on github.
Up to now diagnostics for mixed and hierarchical models have required much programming by the analyst, especially if one desires influence diagnostics. To help fill this need, the R package HLMdiag:
The stable release of HLMdiag is available on CRAN.
HLMdiag is still under development and there are still improvements to be made! You can watch the development of the HLMdiag package on github.