setup.rmd

---
title: "Setup for GLMM course"
---

1. Please make sure you have the **latest version** of R (3.5.1) installed from [CRAN](https://cran.r-project.org/).

2. The RStudio interface is strongly recommended; you can download it [here](https://www.rstudio.com/products/rstudio/download/) (get the free Desktop version).

3. Install primary GLMM-fitting packages (and a variety of extras).
Note that this list deliberately takes an [everything-but-the-kitchen-sink](https://en.wiktionary.org/wiki/everything_but_the_kitchen_sink#English) 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.

```{r pkg1,eval=FALSE}
## 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", "cAIC4", "car", "coda", "DHARMa", "effects",
               "emmeans", "HLMdiag", "Hmisc", "lmerTest", "multcomp",
               "MuMIn", "pbkrtest", "RLRsim", "rockchalk", "sjPlot",
               "sjstats", "stargazer", "texreg")
## 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)
}
devtools::install_github("bbolker/broom.mixed")
devtools::install_github("mjskay/tidybayes")
## 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. 

4. 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](https://cran.r-project.org/bin/windows/Rtools/)
- 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](https://www.moncefbelyamani.com/how-to-install-xcode-homebrew-git-rvm-ruby-on-mac/); you only need to do "Step 1" of these instructions.)
- Linux: make sure you have gcc/g++ installed

5. Install 

---
Last updated: `r Sys.time()`