… ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. …
analysis in R.’ The American Statistician ‘The High-level software language of R is setting …

… In this book I describe the use of R for examining, managing, … -effects parameters and random
effects. Because of the way that we will define random effects, a model with random effects …

… Here, we make a case for the importance of reporting R 2 for mixed-effects models. We first
… We then review previously proposed definitions of R 2 measures for mixed-effects models …

… ) or from guides obtainable from the R website. Even so, a reader unfamiliar with R should
be able to follow the intent of the analysis and learn a little R in the process without further …

… -effects models are being used ever more frequently in the analysis of experimental data.
However, in the lme4 package in R the standards for evaluating significance of fixed effects in …

… We introduce an R package, robustlmm, to robustly fit linear mixed-effects models. The … by
lme4, the R package that implements classic linear mixed-effects model estimation in R. The …

Ultrastructural investigations on mammalian cells cultured in vitro show that R 17934, a new
synthetic anticancer drug, interferes with the structure and function of microtubules, both in …

This method function calculates the empirical autocorrelation function for the residuals from
a gls fit. If a grouping variable is specified in form, the autocorrelation values are calculated …

… The existing R packages, however, currently only provide limited capabilities for conducting
… provides functions for conducting meta-analyses in R and includes the required methods for …

… effects of the antioxidant R‐α‐lipoic acid. Chronic systemic exposure of mice to D‐galactose
(100 mg/kg, sc, 7 weeks) induced a spatial memory deficit, an increase in cell karyopyknosis…