Setup for GLMM course

Please make sure you have the latest version of R (3.5.2) installed from CRAN.
The RStudio interface is strongly recommended; you can download it here (get the free Desktop version).
Install primary GLMM-fitting packages (and a variety of extras). Note that this list deliberately takes an everything-but-the-kitchen-sink approach, since it will save time to have everything you might want installed in advance. If you have questions or problems, please contact me before the workshop.

## modeling packages
mod_pkgs <- c("bbmle", "blme", "brms", "gamm4", "glmmLasso", "glmmML",
              "glmmTMB", "lme4", "MCMCglmm", "robustlmm", "rstanarm", "spaMM")
## miscellaneous/data manipulation
data_pkgs <- c("benchmark", "brglm", "devtools", "emdbook", "MEMSS",
               "plyr", "reshape2", "SASmixed", "tidyverse")
## model processing/diagnostics/reporting
diag_pkgs <- c("afex", "agridat", "AICcmodavg", "aods3", "arm",
               "broom", "broom.mixed", "cAIC4", "car", "coda", "DHARMa",
               "effects", "emmeans", "HLMdiag", "Hmisc", "lmerTest", "multcomp",
               "MuMIn", "pbkrtest", "RLRsim", "rockchalk", "sjPlot",
               "sjstats", "stargazer", "texreg", "tidybayes")
## graphics
graph_pkgs <- c("cowplot", "directlabels",
                "dotwhisker", "GGally", "ggalt", "ggplot2",
                "ggpubr", "ggstance", "gridExtra", "plotMCMC",
                "plotrix", "viridis")

all_pkgs <- c(mod_pkgs,data_pkgs,diag_pkgs,graph_pkgs)
avail_pkgs <- rownames(available.packages())
already_installed <- rownames(installed.packages())
to_install <- setdiff(all_pkgs,already_installed)
if (length(to_install)>0) {
    install.packages(to_install,dependencies=TRUE)
}
## maybe get devel version of broom.mixed?
devtools::install_github("bbolker/broom.mixed")
## get INLA (optional!)
source("http://www.math.ntnu.no/inla/givemeINLA.R")

There is no need to (re)install packages such as grid, nlme, MASS, mgcv, as they come with a standard R installation.

If we end up using the brms package for Bayesian computation, we will need compilers installed as well:

Because brms is based on Stan, a C++ compiler is required. The program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On Mac, you should install Xcode. For further instructions on how to get the compilers running, see the prerequisites section on https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.

Windows: download Rtools from here
MacOS: install Xcode; on recent versions of MacOS you need to open a Terminal, type xcode-select --install and then click “Install” and “Agree”. (If you have an older version or want more details see here; you only need to do “Step 1” of these instructions.)
Linux: make sure you have gcc/g++ installed

Install

Last updated: 2019-01-10 13:29:29