Multilevel Modeling Using R

This is a compilation of lecture notes which is trying to pass as a "textbook". Although authors have tried to give a comprehensive coverage of fitting multilevel models in R, there are numerous issues with the editing:(1) As far as I understand, the datasets referred in the book are not available in R. Therefore, one cannot run the code and replicate the results when reading the book.(2) Earlier in the book, authors keep referring to a chapter on basics of using R which is not there!(3) There are numerous errors in editing which break the continuity of reading and that of mathematical notation. Some examples include sigma_mo^2 (p. 46), erroneous references to table 2.2 (p. 27), probably referring to the same data set by different names (not sure but appears to be so on p. 104), references to equations on p. 218 appear to be out of context, on p. 187 text and R output seem to completely different (confused), on p. 195 it seems x and y variables are flipped in the code, and many many more...(4) The interpretation and meaning of various components of the output is not sufficiently explained for more complex models (for example p. 50-55).(5) No clear distinction is made between lme4 and lmerTest library (examples on p. 104, p. 135).(6) On p. 168-170, model 9.2 is fitted with default settings. No code is provided for modifying the model as discussed in the text.(7) For Bayesian multilevel models, several things are not explained: how to decide re: autocorr, how to handle if trace and density plots are "not nice".