### RevBayes Startup Output

Source: https://revbayes.github.io/workshops/woodshole2018

Example of the console output displayed upon successfully starting RevBayes.

```text
Build from master (94149b) on Mon Jul 23 15:55:28 EDT 2018

Visit the website www.RevBayes.com for more information about RevBayes.

RevBayes is free software released under the GPL license, version 3. Type 'license()' for details.

To quit RevBayes type 'quit()' or 'q()'.


>
```

--------------------------------

### RevBayes Startup Output

Source: https://revbayes.github.io/workshops/woodshole2019

Example output displayed upon successfully starting the RevBayes application.

```text
Build from master (94149b) on Mon Jul 23 15:55:28 EDT 2019

Visit the website www.RevBayes.com for more information about RevBayes.

RevBayes is free software released under the GPL license, version 3. Type 'license()' for details.

To quit RevBayes type 'quit()' or 'q()'.


>
```

--------------------------------

### Install Prerequisites via Homebrew

Source: https://revbayes.github.io/compile-osx

Install CMake and Boost using the Homebrew package manager.

```bash
brew install cmake boost
```

--------------------------------

### Bernoulli Distribution MCMC Setup

Source: https://revbayes.github.io/documentation/dnBernoulli.html

Example of defining a Bernoulli distribution within a RevBayes model and running an MCMC analysis.

```Rev
p ~ dnBeta(1.0,1.0)
x ~ dnBernoulli(p)
x.clamp(1)
moves[1] = mvSlide(p, delta=0.1, weight=1.0)
monitors[1] = screenmonitor(printgen=1000, separator = "        ", x)
mymodel = model(p)
mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.burnin(generations=20000,tuningInterval=100)
mymcmc.run(generations=200000)
```

--------------------------------

### Install Boost via Homebrew

Source: https://revbayes.github.io/developer/setup

Use this command to install the necessary Boost libraries on macOS.

```bash
brew install boost
```

--------------------------------

### Configure and Install Boost

Source: https://revbayes.github.io/compile-windows

Commands to bootstrap and build the Boost library using the GCC toolset on Windows.

```bash
./bootstrap.bat gcc
```

```bash
b2 toolset=gcc-13 address-model=64 architecture=x86 --build-dir=build variant=release --build-type=complete --prefix=c:/boost/boost_1_82_0/gcc --with-regex --with-program_options --with-thread --with-system --with-filesystem --with-date_time --with-serialization  cxxflags=-std=c++17 install
```

--------------------------------

### Geometric Distribution MCMC Setup

Source: https://revbayes.github.io/documentation/dnGeometric.html

Example demonstrating the definition of a geometric distribution within a Bayesian model and the subsequent MCMC configuration.

```Rev
p ~ dnBeta(1.0,1.0)
x ~ dnGeom(p)
x.clamp(10)
moves[1] = mvSlide(p, delta=0.1, weight=1.0)
monitors[1] = screenmonitor(printgen=1000, separator = "        ", p)
mymodel = model(p)
mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.burnin(generations=20000,tuningInterval=100)
mymcmc.run(generations=200000)
```

--------------------------------

### Initialize Starting Tree

Source: https://revbayes.github.io/tutorials/biogeo/biogeo_dating.html

Read the initial tree from the specified file to ensure a non-zero starting probability.

```Rev
tree_init = readTrees(tree_fn)[1]

```

--------------------------------

### Setup Monitors

Source: https://revbayes.github.io/tutorials/mcmc/poisson.html

Configure file and screen monitors to track MCMC progress.

```RevBayes
monitors[1] = mnModel(filename="output/airline_fatalities.log",printgen=10, separator = "	")
monitors[2] = mnScreen(printgen=10, alpha, beta)
```

--------------------------------

### Install build dependencies on Ubuntu

Source: https://revbayes.github.io/compile-linux

Use the apt package manager to install essential build tools, CMake, and Boost libraries.

```bash
sudo apt update
sudo apt install build-essential cmake libboost-all-dev
```

--------------------------------

### Install Homebrew

Source: https://revbayes.github.io/compile-osx

Use this command to install the Homebrew package manager if it is not already present on your system.

```bash
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```

--------------------------------

### Initialize Rate Starting Values

Source: https://revbayes.github.io/tutorials/sse/bisse.html

Manually sets starting values for speciation and extinction rates if the default initialization fails.

```Rev
for (i in 1:NUM_STATES) {
speciation[i].setValue( ln(367.0/2.0) / observed_phylogeny.rootAge() )
extinction[i].setValue( speciation[i]/10.0 )
}
```

--------------------------------

### Start the container with bash entrypoint

Source: https://revbayes.github.io/developer/docker

Launches the container and overrides the entrypoint to provide a bash shell.

```bash
docker run --entrypoint /bin/bash revbayesdockerfiles:ubuntu-latest
```

--------------------------------

### Start RevBayes

Source: https://revbayes.github.io/tutorials/fbd_range

Launch the RevBayes executable from the terminal.

```bash
rb

```

--------------------------------

### simStartingTree(Taxon[] taxa, Clade[] constraints)

Source: https://revbayes.github.io/documentation/simStartingTree.html

Initializes a starting tree using a set of taxa and optional topological constraints.

```APIDOC
## simStartingTree

### Description
Initializes a starting tree for phylogenetic analysis.

### Signature
simStartingTree(Taxon[] taxa, Clade[] constraints)

### Arguments
- **taxa** (Taxon[]) - Required - The taxa used for initialization.
- **constraints** (Clade[]) - Optional - The topological constraints. Default: [ ]

### Return Type
TimeTree
```

--------------------------------

### Format bash console input

Source: https://revbayes.github.io/developer/tutorial

Use the {:.bash} class for bash command examples.

```bash
ls tutorials
grep phylogenetics tutorial.md
```

--------------------------------

### Poisson Distribution MCMC Setup

Source: https://revbayes.github.io/documentation/dnPoisson.html

Demonstrates defining a Poisson distributed variable and configuring an MCMC analysis in Rev.

```Rev
l ~ dnUniform(0.0,100.0)
x ~ dnPoisson(l)
x.clamp(10)
moves[1] = mvSlide(l, delta=0.1, weight=1.0)
monitors[1] = mnScreen(printgen=1000, separator = "        ", l)
mymodel = model(l)
mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.burnin(generations=20000,tuningInterval=100)
mymcmc.run(generations=200000)
```

--------------------------------

### Set Starting Values

Source: https://revbayes.github.io/tutorials/mcmc/poisson.html

Initialize parameter values to avoid likelihood issues during MCMC startup.

```RevBayes
# let us use reasonable starting value
alpha.setValue(0.0)
beta.setValue(0.0)
```

--------------------------------

### Configure Tutorial Metadata

Source: https://revbayes.github.io/developer/tutorial

Example of populated YAML front matter including title, subtitle, authors, and complexity level.

```yaml
---
title: How to science
subtitle: A postmodern experience
authors:
- Alice
- Bob
level: 0
---
```

--------------------------------

### Load Required Libraries

Source: https://revbayes.github.io/tutorials/intro/revgadgets.html

Load the necessary R packages for the tutorial. Ensure these are installed in your R environment before execution.

```R
library(RevGadgets)
library(coda)
library(ggplot2)
library(ggtree)
library(grid)
library(gridExtra)
```

--------------------------------

### Set-Up and Run MCMC

Source: https://revbayes.github.io/tutorials/morph_ase/corr.html

Initializes the MCMC object with the model and monitors, then executes the simulation.

```RevBayes
# Initialize the MCMC object #
    mymcmc = mcmc(mymodel, monitors, moves, nruns=2, combine="mixed")

    # Run the MCMC #
    mymcmc.run(generations=5000, tuningInterval=200)
```

--------------------------------

### Define a Lognormal Distribution in Rev

Source: https://revbayes.github.io/documentation/dnLognormal.html

Example showing how to set parameters and initialize a lognormal distribution variable.

```Rev
# set an expected value for x
expectation_of_x <- 1
# set a mean and sd parameter
sd <- 0.5
mean <- ln(expectation_of_x) - 0.5 * sd^2
# create a lognormal distribution with expected value of 1
x ~ dnLognormal(mean=mean, sd=sd)
```

--------------------------------

### Constructing the FMutSel0 model

Source: https://revbayes.github.io/documentation/fnFMutSel0.html

Example demonstrating the initialization of parameters and the construction of the FMutSel0 rate matrix compared to an alternative model construction.

```Rev
er ~ dnDirichlet( v(1,1,1,1,1,1) )
nuc_pi ~ dnDirichlet( rep(2.0, 4) )
F ~ dnIID(20, dnNormal(0,1))
omega ~ dnUniform(0,1)
# The FMutSel0 model from Yang and Nielsen (2008)
Q1 := fnFMutSel0(fnGTR(er, nuc_pi), F, omega)

# The same -- fMutSel0 = GTR(er,nuc_pi) + X3 + MutSel(F) + dNdS(omega)
Q2 := fndNdS( fnMutSelAA( fnX3( fnGTR(er, nuc_pi)), F), omega)
```

--------------------------------

### Configure MCMC Stationarity Stopping Rule

Source: https://revbayes.github.io/documentation/srStationarity.html

Example demonstrating the setup of a binomial model and the application of the srStationarity stopping rule within an MCMC run.

```Rev
# Binomial example: estimate success probability given 7 successes out of 20 trials
r ~ dnExp(10)
p := Probability(ifelse(r < 1, r, 1))
n <- 20
k ~ dnBinomial(n, p)
k.clamp(7)
mymodel = model(k)

moves = VectorMoves()
moves.append( mvSlide(r, delta=0.1, weight=1) )

paramFile = "parameters.log"

monitors = VectorMonitors()
monitors.append( mnModel(filename=paramFile, printgen=100, p) )

# Stop when stationarity has been attained at confidence level gamma = 0.25
stopping_rules[1] = srStationarity(prob = 0.25, file = paramFile, freq = 1000)

# Create the MCMC object
# Set nruns = 2 to ensure the stationarity statistic is applicable
mymcmc = mcmc(mymodel, monitors, moves, nruns = 2)

# Begin the MCMC run
mymcmc.run(rules = stopping_rules)
```

--------------------------------

### Read MCMC log files

Source: https://revbayes.github.io/documentation/readTrace.html

Examples demonstrating how to load single or multiple log files into trace objects.

```Rev
# read in a single log file as a vector of traces
traces = readTrace("out.log", burnin=0.5)

# read in two log files called 'out_run_1.log', 'out_run_2.log'
traces = readTrace("out.log", burnin=0.5, nruns=2)
```

--------------------------------

### Install RevGadgets and ggtree Dependencies

Source: https://revbayes.github.io/tutorials/sse/bisse.html

Installs necessary R packages for visualization.

```R
install.packages("devtools")
library(devtools)
install_github("GuangchuangYu/ggtree")
install_github("revbayes/RevGadgets")
```

--------------------------------

### Install Revticulate package

Source: https://revbayes.github.io/interfaces

Use the remotes package to install Revticulate from GitHub.

```r
remotes::install_github("revbayes/Revticulate")
```

--------------------------------

### Install YCM dependencies

Source: https://revbayes.github.io/developer/setup

Install required system packages for YouCompleteMe on Ubuntu 16.04.

```bash
sudo apt-get install build-essential cmake python-dev python3-dev clang
```

--------------------------------

### Download Tutorial Data

Source: https://revbayes.github.io/workshops/woodshole2019

Use wget to retrieve specific data files required for workshop tutorials.

```bash
wget https://revbayes.github.io/tutorials/fbd/data/bears_cytb.nex
```

--------------------------------

### Configure Output Paths

Source: https://revbayes.github.io/tutorials/host_rep/host_rep.html

Set up output directory and filename strings.

```Rev
out_str = "out.time"
	out_fn = "output/" + out_str
```

--------------------------------

### Install RevGadgets via devtools

Source: https://revbayes.github.io/tutorials/intro/revgadgets.html

Use devtools to install the package directly from the GitHub repository.

```R
install.packages("devtools")
devtools::install_github("cmt2/RevGadgets")
```

--------------------------------

### Initialize and Run MCMC Analysis

Source: https://revbayes.github.io/documentation/mcmc.html

Demonstrates creating a model, setting up moves and monitors, running an MCMC analysis, and resuming from a checkpoint.

```Rev
# Create a simple model (unclamped)
a ~ dnExponential(1)
mymodel = model(a)

# Create a move vector and a monitor vector
moves[1] = mvScale( a, lambda = 1.0, weight = 1.0 )
monitors[1] = mnFile( a, filename = "output/out.log" )

# Create an mcmc object
mymcmcObject = mcmc( mymodel, monitors, moves )

# Run a short analysis
mymcmcObject.burnin( generations = 400, tuningInterval = 100 )
mymcmcObject.run( generations = 400, checkpointFile = "output/out.ckp", checkpointInterval = 100 )

# print the summary of the operators (now tuned)
mymcmcObject.operatorSummary()

# Resume analysis from the checkpoint file
mymcmcObject.initializeFromCheckpoint( "output/out.ckp" )

# Conduct an additional 400 generations
mymcmcObject.run( generations = 400 )

# Stopping rules are defined on the total number of generations
# This command will have no effect, as 400 generations have already been performed.
mymcmcObject.run( rules = [ srMaxIteration(400) ] )
```

--------------------------------

### Constructing FMutSel and related models

Source: https://revbayes.github.io/documentation/fnFMutSel.html

Demonstrates the initialization of parameters and the construction of the FMutSel model compared to a standard dNdS approach.

```Rev
er ~ dnDirichlet( v(1,1,1,1,1,1) )
nuc_pi ~ dnDirichlet( rep(2.0, 4) )
F ~ dnIID(61, dnNormal(0,1))
omega ~ dnUniform(0,1)
# The FMutSel model from Yang and Nielsen (2008)
Q1 := fnFMutSel(fnGTR(er, nuc_pi), F, omega)

# The same -- fMutSel = GTR(er,nuc_pi) + X3 + MutSel(F) + dNdS(omega)
Q2 := fndNdS(fnMutSel(F, fnX3(fnGTR(er, nuc_pi))), omega)
```

--------------------------------

### Modularize Tutorial Content

Source: https://revbayes.github.io/developer/tutorial

Include external Markdown files into a main tutorial page using the include_relative Liquid tag.

```markdown
This is an external *Markdown* file
without [YAML](http://camel.readthedocs.io/en/latest/yamlref.html) front matter.

This *file* will be __formatted__ ~~as plain text~~ like the rest of the main text.

You can even include $\LaTeX$.
```

```liquid
{% include_relative external_file.md %}
```

--------------------------------

### Install and run RevBayes development version

Source: https://revbayes.github.io/compile-spack

Install the development branch of RevBayes with or without MPI support.

```bash
spack install revbayes@develop ~mpi
spack load revbayes
rb
```

```bash
spack install revbayes@develop +mpi
spack load revbayes
mpirun rb-mpi
```

--------------------------------

### Link Between Tutorials

Source: https://revbayes.github.io/developer/tutorial

Reference other tutorials using Liquid tags or filters to maintain consistent navigation.

```liquid
This is a reference to the {% page_ref intro %} tutorial.

{% assign var = "ctmc" %}
This how to link to the {% page_ref {{ var }} %} tutorial using a liquid variable,

{% assign tut = "clocks/simple" | match_page %}
This is how to retrieve the *{{ tut.title }}* tutorial page by name.

The relative URL of the `{{ var }}` tutorial is `{% page_url {{ var }} %}`.
```

--------------------------------

### Install Revticulate via CRAN

Source: https://revbayes.github.io/tutorials/tutorial_structure

Standard installation method for the Revticulate package using the R package manager.

```R
install.packages("Revticulate")
```

--------------------------------

### Install R packages for Revticulate

Source: https://revbayes.github.io/interfaces

Install the necessary R packages to enable RevBayes integration within RStudio.

```r
install.packages("remotes")
install.packages("usethis")
```

--------------------------------

### Initialize Model and Data for Simulation

Source: https://revbayes.github.io/tutorials/pps_morpho

Sets up the input file, analysis name, and model parameters required for morphological data simulation.

```Rev
inFile = "data/Agnolin_2007a_paleobiodb.nex"
analysis_name = "pps_morpho_example"
morpho <- readDiscreteCharacterData(inFile)
model_name = "Mk"
num_states ="4"
source( model_file_name )
```

--------------------------------

### Create and run a Docker container

Source: https://revbayes.github.io/tutorials/docker

Initializes a new container with a mounted host directory for file persistence.

```bash
~ $ ### from terminal on my Mac laptop (host)
~ $ # create and run new Docker container
~ $ docker run --name phylodocker_demo \
               --volume /Users/mlandis/projects/docker_test:/docker_test \
               -it sswiston/phylo_docker:full_amd64 \
               /bin/sh
```

--------------------------------

### Clone and install RevBayes Jupyter Kernel

Source: https://revbayes.github.io/interfaces

Commands to clone the kernel repository and install it for use in Jupyter Notebooks.

```bash
git clone https://github.com/revbayes/revbayes_kernel.git
```

```bash
sudo python3 setup.py install
python3 -m revbayes_kernel.install
pip3 install metakernel
```

--------------------------------

### Simulate a tree with dnCBDSP

Source: https://revbayes.github.io/documentation/dnCBDSP.html

Demonstrates initializing process parameters and simulating a tree using the dnCBDSP distribution.

```Rev
# draw basic process parameters
taxa <- [taxon("A"), taxon("B"), taxon("C"), taxon("D"), taxon("E")]
root_age ~ dnUniform(0, 2)
root_lambda ~ dnUniform(0, 1)
root_mu ~ dnUniform(0, 1)
sampling_prob <- 1

# simulate tree
tree ~ dnCBDSP(rootAge    = root_age,
               rootLambda = root_lambda,
               rootMu     = root_mu,
               delta      = 0.2,
               rho        = sampling_prob,
               condition  = "survival",
               taxa       = taxa)
```

--------------------------------

### Install CMake via Homebrew

Source: https://revbayes.github.io/developer/setup

Use this command to install the CMake build tool on macOS via Homebrew.

```bash
brew install cmake
```

--------------------------------

### Install and run RevBayes stable version

Source: https://revbayes.github.io/compile-spack

Install the latest stable release of RevBayes with or without MPI support.

```bash
spack install revbayes ~mpi
spack load revbayes
rb
```

```bash
spack install revbayes +mpi
spack load revbayes
mpirun rb-mpi
```

--------------------------------

### Monitor and Run MCMC

Source: https://revbayes.github.io/tutorials/coalescent/GMRF.html

Sets up file and screen monitors, initializes the MCMC object, and executes the analysis.

```Rev
monitors.append( mnModel(filename="output/horses_iso_GMRF.log",printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_iso_GMRF.trees",psi,printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_iso_GMRF_NEs.log",population_size,printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_iso_GMRF_times.log",interval_times,printgen=THINNING) )
monitors.append( mnScreen(population_size, root_age, printgen=100) )

mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.burnin(NUM_MCMC_ITERATIONS*0.1,100)
mymcmc.run(NUM_MCMC_ITERATIONS, tuning = 100)
```

--------------------------------

### Set Starting Tree Values

Source: https://revbayes.github.io/tutorials/biogeo/biogeo_dating.html

Initializes the tree and root age with a starting tree to ensure non-zero likelihood.

```Rev
tree.setValue(tree_init)
root_age.setValue(tree_init.rootAge())
```

--------------------------------

### Manage Tutorial Files

Source: https://revbayes.github.io/developer/tutorial

Include or exclude specific files from the Data files and scripts box using YAML front matter attributes.

```yaml
include_files:
    - ctmc/data/primates_and_galeopterus_cytb.nex
exclude_files:
    - data/useless_file.nex

```

--------------------------------

### Install ImageMagick dependency

Source: https://revbayes.github.io/tutorials/intro/revgadgets.html

Install the external ImageMagick software required by the magick R package dependency on macOS or Linux.

```bash
brew install imagemagick
```

--------------------------------

### Geometric Random Walk MCMC Setup

Source: https://revbayes.github.io/documentation/mvRandomGeometricWalk.html

Demonstrates initializing a geometric distribution variable and configuring the move within an MCMC analysis.

```Rev
p <- 0.8
x ~ dnGeom(p)

moves[1] = mvRandomGeometricWalk(x, weight=1.0)
monitors[1] = mvScreen(printgen=1000, x)

mymodel = model(p)
mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.burnin(generations=20000,tuningInterval=100)
mymcmc.run(generations=200000)
```

--------------------------------

### Define Modular Tutorial Structure

Source: https://revbayes.github.io/developer/tutorial

Organize tutorial components into a directory structure where only the main entry points contain YAML front matter.

```text
new_tutorial
├── data/
├── scripts/
├── figures/
├── modules/ <-- module files (no front matter)
│   ├── intro.md
│   ├── exercise1.md
│   ├── exercise2.md
│   └── exercise3.md
├── new_tutorial.md <-- tutorial page (with front matter)
└── v2.md           <-- tutorial page (with front matter)
```

--------------------------------

### Setup and run MCMC simulation

Source: https://revbayes.github.io/tutorials/pomos

Initializes the MCMC object with the model, monitors, and moves, then executes the simulation for a specified number of generations.

```RevBayes
pomo_mcmc = mcmc(pomo_model, monitors, moves, nruns=2, combine="mixed")
```

```RevBayes
pomo_mcmc.run( generations=100000 )
```

--------------------------------

### Install build dependencies on CentOS 8

Source: https://revbayes.github.io/compile-linux

Use the yum package manager to install development tools, CMake, and Boost development headers.

```bash
sudo yum group install "Development Tools"
sudo yum install cmake boost-devel
```

--------------------------------

### Configure MCMC Monitors and Run

Source: https://revbayes.github.io/tutorials/coalescent/GMRF_treebased.html

Set up model monitors and execute the MCMC analysis.

```Rev
monitors.append( mnModel(filename="output/horses_iso_GMRF_treebased.log",printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_iso_GMRF_treebased_NEs.log",population_size,printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_iso_GMRF_treebased_times.log",interval_times,printgen=THINNING) )
monitors.append( mnScreen(population_size, printgen=100) )

mymcmc = mcmc(mymodel, monitors, moves)
mymcmc.burnin(NUM_MCMC_ITERATIONS*0.1,100)
mymcmc.run(NUM_MCMC_ITERATIONS, tuning = 100)
```

--------------------------------

### Example distance matrix

Source: https://revbayes.github.io/tutorials/biogeo/biogeo_epoch.html

Sample content of a distance matrix file.

```Text
    001 145 239 419
    145 001 059 239
    239 059 001 082
    419 239 082 001
```

--------------------------------

### Example connectivity matrix

Source: https://revbayes.github.io/tutorials/biogeo/biogeo_epoch.html

Sample content of an area connectivity file.

```Text
    1 1 1 0
    1 1 1 0
    1 1 1 0
    0 0 0 0
```

--------------------------------

### Initialize MCMC loop

Source: https://revbayes.github.io/tutorials/mcmc/binomial.html

Define the number of repetitions and start the iteration loop.

```R
# Write the MH algorithm
reps = 10000
for(rep in 1:reps){
```

--------------------------------

### Create and run a model analysis

Source: https://revbayes.github.io/documentation/model.html

Demonstrates initializing a model, generating a graph visualization, setting up MCMC components, and running an analysis. Note that workspace variables remain unchanged after the analysis.

```Rev
# Create a simple model (unclamped)
a ~ dnExponential(1)
b ~ dnExponential(a)
mymodel = model(b) # equivalent to model(a) or model(a, b)

# Save a DOT visualization of the model to file
mymodel.graph("mymodel.dot")

# Create a move vector and a monitor vector
moves = [ mvScale( a, lambda = 1.0, weight = 1.0 ) ]
monitors = [ mnScreen(printgen = 10, a) ]

# Create an mcmc object
mymcmcObject = mcmc( mymodel, monitors, moves )

# Print value of a
print(a)

# Run a short analysis
mymcmcObject.run( generations = 100 )

print(a) # Value is unchanged in the workspace - only the copy is modified
```

--------------------------------

### Start GDB debugging

Source: https://revbayes.github.io/developer/setup

Launch the RevBayes executable within the GDB debugger.

```bash
gdb rb
```

--------------------------------

### MCMC Burn-in Output

Source: https://revbayes.github.io/tutorials/pps_morpho

Example console output during the MCMC burn-in phase.

```text
Running burn-in phase of Monte Carlo sampler for 200 iterations.
   This simulation runs 2 independent replicates.
   The simulator uses 4 different moves in a random move schedule with 30.7 moves per iteration


Progress:
0---------------25---------------50---------------75--------------100
********************************************************************
```

--------------------------------

### Initialize and Run HillClimber Analysis

Source: https://revbayes.github.io/documentation/HillClimber.html

Demonstrates setting up a model, defining moves and monitors, and executing a HillClimber analysis.

```Rev
# Create a simple model (unclamped)
a ~ exponential(1)
mymodel = model(a)

# Create a move vector and a monitor vector
moves[1] = mvScale(a, lambda=1.0, weight=1.0)
monitors[1] = mnFile(a,"output/out.log")

# Create an HillClimber object
myHillClimberObject = HillClimber( mymodel, monitors, moves)

# Run a short analysis
myHillClimberObject.burnin( generations = 400, tuningInterval = 100)
myHillClimberObject.run( generations = 400)

# print the summary of the operators (now tuned)
myHillClimberObject.operatorSummary()
```

--------------------------------

### Set Working Directory

Source: https://revbayes.github.io/tutorials/fbd_simple

Changes the current working directory to the location of the tutorial files.

```Rev
setwd("<path to>/RB_FBD_Tutorial")
```

--------------------------------

### Display Summary Output

Source: https://revbayes.github.io/tutorials/intro/revgadgets.html

Example of the console output generated by the summarizeTrace function.

```text
$`pi[1]`
$`pi[1]`$trace_1
        mean        median        MAP  
        0.3280593   0.3282250     0.3265728      
        quantile_2.5  quantile_97.5 
        
        0.3080363     0.3481651
        
...

$`pi[4]`
$`pi[4]`$trace_1
        mean        median        MAP  
        0.2152190   0.2145595     0.2144797
        
        quantile_2.5  quantile_97.5
        0.2020019     0.2278689
```

--------------------------------

### Get Number of Taxa

Source: https://revbayes.github.io/tutorials/sse/bisse.html

Extracts the number of sampled species from the phylogeny object.

```Rev
num_taxa <- observed_phylogeny.ntips()
```

--------------------------------

### Create instruction box

Source: https://revbayes.github.io/developer/tutorial

Use the {:.instruction} class to create an instruction callout.

```markdown
> Lorem ipsum dolor sit amet, consectetur adipiscing elit,
> sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
{:.instruction}
```

--------------------------------

### Run a RevBayes terminal

Source: https://revbayes.github.io/developer/docker

Starts a RevBayes terminal session within a temporary container.

```bash
docker run –rm -it docker.pkg.github.com/wadedismukes/revbayes-dockerfiles/revbayesdockerfiles:ubuntu-latest
```

--------------------------------

### Create Bash Profile

Source: https://revbayes.github.io/compile-osx

Initialize a .bash_profile file in your home directory if it does not exist.

```bash
cd ~
touch .bash_profile
nano .bash_profile
```

--------------------------------

### Initialize Model and MCMC

Source: https://revbayes.github.io/tutorials/biogeo/biogeo_simple.html

Create the model object and execute the MCMC analysis.

```Rev
mymodel = model(m_bg)
```

```Rev
mymcmc = mcmc(mymodel, moves, monitors)
mymcmc.run(3000)
```

--------------------------------

### Example Covarion Rate Matrix Output

Source: https://revbayes.github.io/tutorials/morph/morph_more.html

Displays the resulting rate matrix structure.

```text
[ [ -1.1126, 0.8901, 0.2225, 0.0000 ] ,
     0.8901, -1.1126, 0.0000, 0.2225 ] ,
     0.4451, 0.0000, -1.0385, 0.5934 ] ,
     0.0000, 0.4451, 0.1978, -0.6429 ] ]
```

--------------------------------

### View Ancestral States Output

Source: https://revbayes.github.io/tutorials/morph_ase/ase.html

Example format of the ancestral states output file.

```text
Iteration	end_1	end_2	end_3	end_4	end_5	...
0	0	0	1	0	1	...
1	0	0	1	0	1	...
2	0	0	0	0	1	...
3	0	0	1	0	1	...
4	0	0	1	0	1	...
5	0	0	0	1	1	...
6	0	0	1	0	1	...
7	0	1	1	0	1	...
8	0	0	1	1	1	...
9	0	0	0	0	1	...
10	0	0	1	0	1	...
...
```

--------------------------------

### Example Output Log

Source: https://revbayes.github.io/tutorials/fbd_simple

Displays the console output generated during the execution of the MCMC simulation.

```text
   Processing file "FBD_tutorial.Rev"
   Successfully read one character matrix from file 'data/bears_morphology.nex'

   Running MCMC simulation
   This simulation runs 1 independent replicate.
   The simulator uses 11 different moves in a random move schedule with 84 moves per iteration
   ...
```

--------------------------------

### Initialize and run an MCMCMC analysis

Source: https://revbayes.github.io/documentation/mcmcmc.html

Demonstrates creating a model, defining moves and monitors, initializing the mcmcmc object, and executing the analysis.

```Rev
# Create a simple model (unclamped)
a ~ dnExponential(1)
mymodel = model(a)

# Create a move vector and a monitor vector
moves[1] = mvScale(a, lambda=1.0, weight=1.0)
monitors[1] = mnFile(a,"output/out.log")

# Create an mcmcmc object
myMcmcmcObject = mcmcmc( mymodel, monitors, moves, nchains=4, deltaHeat=5)

# Run a short analysis
myMcmcmcObject.burnin( generations = 400, tuningInterval = 100)
myMcmcmcObject.run( generations = 400)

# print the summary of the operators (now tuned)
myMcmcmcObject.operatorSummary()
```

--------------------------------

### HSMRF Prior Initialization

Source: https://revbayes.github.io/tutorials/stairwayplot

Initial setup for Horseshoe Markov Random Field priors.

```Rev
avmvn = mvAVMVN(weight=100)

log_theta_at_present ~ dnUniform(-20,2)
log_theta_at_present.setValue( ln( 0.001 ) )

moves.append( mvSlideBactrian(log_theta_at_present,weight=5) )
avmvn.addVariable(log_theta_at_present)


## Global shrinkage parameter
global_scale_hyperprior     <- 0.021
```

--------------------------------

### Setup dnAutocorrelatedEvent Distribution

Source: https://revbayes.github.io/tutorials/coalescent/Skyfish

Configure the distribution combining population sizes and interval change-points.

```Rev
events ~ dnAutocorrelatedEvent(eventDistribution = dnPoisson(lambda=10),
                               valueDistribution =[dnUniform(0.0,MAX_AGE),
                                                   dnLognormal( ln(EXPECTED_POP_SIZE), sd=2*H )],
                               names=["time","theta"],
                               minNumberEvents=[0,1],
                               autocorrelationTypes=["NONE","ACLN"],
                               autocorrelationDependencies=["none","time"],
                               autocorrelationSigmas=[0,ac_sigma],
                               sort = "time")
```

--------------------------------

### Read the tree and initialize variables

Source: https://revbayes.github.io/tutorials/divrate/env.html

Load the observed tree and initialize the containers for moves and monitors.

```Rev
T <- readTrees("data/primates_tree.nex")[1]
```

```Rev
taxa <- T.taxa()
```

```Rev
moves    = VectorMoves()
monitors = VectorMonitors()
```

--------------------------------

### Compile YCM plugin

Source: https://revbayes.github.io/developer/setup

Run the installation script to compile the YCM component with Clang support.

```bash
cd YouCompleteMe/
./install.py --clang-completer
```

--------------------------------

### Initialize moves and monitors

Source: https://revbayes.github.io/tutorials/biogeo/biogeo_epoch.html

Create containers for tracking MCMC moves and monitors.

```Rev
    moves = VectorMoves()
    monitors = VectorMonitors()
```

--------------------------------

### Manage specific RevBayes versions

Source: https://revbayes.github.io/compile-spack

Install a specific version of RevBayes or list all available versions.

```bash
spack install revbayes@1.0.12 ~mpi.
```

```bash
spack info revbayes
```

--------------------------------

### Initialize and Run MCMC

Source: https://revbayes.github.io/tutorials/sse/fossils.html

Create the MCMC object and execute the run with optional stopping rules and checkpointing.

```Rev
mymcmc = mcmc(mymodel, monitors, moves)
```

```Rev
stopping_rules[1] = srMinESS(250, file = "output/canidae_diet_bisse.log", freq = 10000)
```

```Rev
mymcmc.run(rules = stopping_rules, checkpointInterval = 1000, checkpointFile = "output/canidae_diet_bisse.state")
```

```Rev
mymcmc.initializeFromCheckpoint("output/canidae_diet_bisse.state")
```

--------------------------------

### Initialize MCMC Parameters

Source: https://revbayes.github.io/tutorials/mcmc/poisson.html

Set starting values for the chain, ensuring they result in a valid likelihood.

```Rev
alpha[1] <- -0.01     # you can also use runif(-1.0,1.0)
beta[1] <- -0.01      # you can also use runif(-1.0,1.0)
```

--------------------------------

### Configure Monitors

Source: https://revbayes.github.io/tutorials/coalescent/heterochronous

Set up model, file, and screen monitors to track parameters and output.

```Rev
monitors.append( mnModel(filename="output/horses_het_Constant.log",printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_het_Constant.trees",psi,printgen=THINNING) )
monitors.append( mnFile(filename="output/horses_het_Constant_NE.log",pop_size,printgen=THINNING) )
monitors.append( mnScreen(pop_size, root_age, printgen=100) )
```

--------------------------------

### Generate Rate Matrices with fnFreeK

Source: https://revbayes.github.io/documentation/fnFreeK.html

Examples demonstrating the creation of 2-by-2 and 3-by-3 rate matrices using vector and matrix inputs, with and without normalization.

```Rev
# Define vector to pass, this will create a 2-by-2 matrix
x <- v(0.5, 0,5)
# Use fnFreeK to create rate matrix
# Note the second argument is true in this case so rates will be normalized
fnFreeK(x)
[ [ -1.0000, 1.0000 ] ,
  [ 1.0000, -1.0000 ] ]

# Case where rates are not normalized
x <- v(0.5, 0.5)
fnFreeK(x, false)
[ [ -0.5000, 0.5000 ] ,
  [ 0.5000, -0.5000 ] ]

# Define matrix for 3-by-3 rate matrix object
x <- v([0, .6, .4], [.2, 0, .4], [.3, .3, 0])
# Create rate matrix object
fnFreeK(x)
[ [ -1.0000, 0.6000, 0.4000 ] ,
  [ 0.2000, -0.6000, 0.4000 ] ,
  [ 0.3000, 0.3000, -0.6000 ] ]
```