Functions to back-fit fixed effects (on F or t values as
well as log-likelihood ratio testing (llrt), AIC, BIC,
relLik.AIC or relLik.BIC) and to forward-fit random effects
(using log-likelihood ratio testing). NOTE that the back- and
forward-fitting of generalized linear mixed-effects regression
(glmer) models is now supported by functions
``bfFixefLMER_t.fnc'' and ``ffRanefLMER.fnc''. The package also
includes a function to compute ANOVAs with upper- and
lower-bound p-values (anti-conservative and conservative,
respectively), a function to graph model criticism plots,
functions to trim data on model residuals or on a response
variable (per subject), a function to perform posthoc analyses
(with or without MCMC p-values), a function to generate
summaries of mcposthoc objects, a function to generate
(dynamic) 3d plots of (i) predicted values of an LMER model for
interactions between two numeric variables,(ii) the raw data as
a function of two numeric variable, and (iii) kernel density
estimates (densities) of two numeric variables, and finally a
function to calculate the relative log-likelihood between two
models.

Antoine Tremblay, Dalhousie University, and Johannes Ransijn, University of Copenhagen
Functions to back-fit fixed effects (on F or t values as well as log-likelihood ratio testing (llrt), AIC, BIC, relLik.AIC or relLik.BIC) and to forward-fit random effects (using log-likelihood ratio testing). NOTE that the back- and forward-fitting of generalized linear mixed-effects regression (glmer) models is now supported by functions ``bfFixefLMER_t.fnc'' and ``ffRanefLMER.fnc''. The package also includes a function to compute ANOVAs with upper- and lower-bound p-values (anti-conservative and conservative, respectively), a function to graph model criticism plots, functions to trim data on model residuals or on a response variable (per subject), a function to perform posthoc analyses (with or without MCMC p-values), a function to generate summaries of mcposthoc objects, a function to generate (dynamic) 3d plots of (i) predicted values of an LMER model for interactions between two numeric variables,(ii) the raw data as a function of two numeric variable, and (iii) kernel density estimates (densities) of two numeric variables, and finally a function to calculate the relative log-likelihood between two models.