### Example Output of Package Version Check

Source: https://statnet.org/workshop-relevent/relevent_tutorial.html

This is an example output showing the version number of the 'relevent' package.

```text
[1] '1.2.1'
```

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

### Install necessary packages

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

Installs the 'htmlwidgets' and 'latticeExtra' packages, which provide additional graphing utilities.

```R
install.packages('htmlwidgets')
install.packages('latticeExtra')
```

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

### Access FFmpeg Installation Help

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Displays the help page for installing FFmpeg, which is required for saving animations as video files using the 'animation' package.

```R
?install.ffmpeg   # help page for installing ffmpeg
```

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

### Install the Entire Statnet Suite

Source: https://statnet.org/workshop-intro-sna-tools

Installs all packages within the Statnet suite. This is an optional step for this tutorial but provides a comprehensive set of tools for network analysis.

```r
install.packages('statnet')
```

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

### Example Version Output in R

Source: https://statnet.org/workshop-relevent/sunbelt_2022_relevent_tutorial.html

This is an example output showing the version number of the 'relevent' package.

```text
[1] '1.1'
```

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

### Check Java Installation

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Verify if Java is correctly installed by checking its version from the command line. Ensure Java is added to your system's PATH environment variable.

```bash
system('java -version')
```

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

### Install Statnet Meta-Package

Source: https://statnet.org/install-statnet

Use this command to install the core Statnet packages and their dependencies from CRAN. This is recommended for beginners.

```r
>  install.packages("statnet")

```

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

### Install statnetWeb Package

Source: https://statnet.org/nme/d0-install.html

Install the statnetWeb package using this command. This is a separate installation from the main EpiModel package.

```r
install.packages("statnetWeb")
```

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

### Install Statnet Packages in R

Source: https://statnet.org/workshop-relevent-experiments/in_silico_experiments_tutorial.RData

Installs the 'relevent', 'sna', and 'parallel' R packages. Ensure you have R installed and an internet connection.

```R
install.packages(c("relevent","sna","parallel"))
```

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

### Install ndtv Package

Source: https://statnet.org/nme/d3-s8-ndtv.R

Install the ndtv package from CRAN. If CRAN binaries are unavailable, use remotes to install from GitHub.

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

```R
# install this package in case you do not already have it installed
install.packages("remotes")
remotes::install_github("statnet/ndtv", subdir = "ndtv")
```

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

### Check Installed Package Versions

Source: https://statnet.org/nme/d0-install.html

Verify the installation and check the versions of installed EpiModel, statnet, and related packages. This command displays detailed session information.

```r
sessionInfo()
```

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

### Install statnet Packages

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

Installs the ergmgp, sna, tsna, and ndtv R packages along with their dependencies. Ensure R is installed and configured before running.

```r
install.packages(c("ergmgp","sna","tsna","ndtv"))
```

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

### Install Statnet Packages in R

Source: https://statnet.org/workshop-relevent/sunbelt_2022_relevent_tutorial.html

Installs the 'relevent' and 'sna' packages, which are essential for relational event modeling with Statnet. 'sna' will automatically install 'network' if needed.

```r
install.packages(c("relevent","sna"))
```

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

### Install EpiModel Package

Source: https://statnet.org/nme/d0-install.html

Use this command to install the EpiModel package and its dependencies. R may prompt you to select a mirror site.

```r
install.packages("EpiModel", dependencies = TRUE)
```

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

### Install R Package

Source: https://statnet.org/nme/d0-tutR.html

Installs a specified package from CRAN, including any necessary dependencies.

```r
install.packages("packagename")
```

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

### Install Core Network Analysis Packages in R

Source: https://statnet.org/workshop-intro-sna-tools

Installs essential R packages for network analysis, including 'network', 'sna', 'igraph', 'tidygraph', 'ggraph', 'intergraph', and 'remotes'. Ensure R and RStudio are installed first.

```r
install.packages(c("network", "sna", "igraph", "tidygraph", "ggraph", 
                   "intergraph", "remotes"))
```

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

### Get List of tsna Package Functions

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

To see all available functions in the tsna package, use the help.start() function.

```r
help(package="tsna")
```

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

### Verbose Simulation Output Example

Source: https://statnet.org/workshop-relevent/sunbelt_2022_relevent_tutorial.html

This output shows the progress of the simulation process, indicating which event is currently being processed.

```text
Working on event 25 of 481 
Working on event 50 of 481 
Working on event 75 of 481 
Working on event 100 of 481 
Working on event 125 of 481 
Working on event 150 of 481 
Working on event 175 of 481 
Working on event 200 of 481 
Working on event 225 of 481 
Working on event 250 of 481 
Working on event 275 of 481 
Working on event 300 of 481 
Working on event 325 of 481 
Working on event 350 of 481 
Working on event 375 of 481 
Working on event 400 of 481 
Working on event 425 of 481 
Working on event 450 of 481 
Working on event 475 of 481 

```

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

### Install and Load ndtv Package

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Installs the ndtv package and its dependencies from CRAN, then loads it along with animation and networkDynamic. Requires R and potentially non-R external dependencies like FFmpeg for video output.

```r
install.packages('ndtv',repos='http://cran.us.r-project.org', 
                 dependencies=TRUE)
library(ndtv) # also loads animation and networkDynamic
```

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

### Load Example Dynamic Network Data

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

Loads the 'short.stergm.sim' dataset, which is an example of a simulated dynamic network generated by the tergm package.

```r
data(short.stergm.sim)
```

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

### Install Individual Statnet Package

Source: https://statnet.org/install-statnet

Use this command to install a specific package from the Statnet suite, such as 'ergm', along with its dependencies. This is useful if you only need certain functionalities.

```r
install.packages("ergm")

```

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

### Load ndtv Package and Dependencies

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Loads the ndtv package and its dependencies. Ensure the package is installed before running.

```r
library(ndtv)
```

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

### Load Statnet Package

Source: https://statnet.org/install-statnet

After installation, use this command to load the Statnet meta-package into your R session for use.

```r
> library(statnet)

```

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

### Check ergmgp Package Version

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

Retrieves and displays the installed version of the ergmgp package. This is useful for ensuring compatibility with tutorial examples.

```r
packageVersion("ergmgp")
```

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

### Load Florentine Network Data

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

Loads the Florentine marriage network dataset. This is a common starting point for network analysis examples.

```R
data(florentine)
```

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

### Access R Package Vignettes

Source: https://statnet.org/nme/d0-tutR.html

Opens the vignette documentation for a specified R package, providing detailed examples and usage guides.

```r
browseVignettes("network")
```

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

### Simulate SI Disease with Strong Intervention

Source: https://statnet.org/nme/d4-s6-Vax.R

Simulates an SI disease with a highly effective intervention starting at week 25. This setup is for a closed population.

```R
param <- param.net(inf.prob = 0.5, inter.eff = 0.96, inter.start = 25)
init <- init.net(i.num = 5)
control <- control.net(type = "SI", nsteps = 100, nsims = 10, ncores = 5)
sim <- netsim(est, param, init, control)
plot(sim)
```

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

### Initialize Network and Simulate Trajectory

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

Sets up a network with vertex attributes and then simulates a trajectory using the simEGP function, returning the simulation history.

```R
set.seed(1331)
net <- network(rgraph(50, tp=1.5/49, mode="graph"), directed = FALSE)
net %v% "x" <- sample(0:1, 50, replace = TRUE)

set.seed(1331)
simtgh <- simEGP(form = tgform, coef = tgcoef, time = 100, 
    process = "CSTERGM", return.history = TRUE) 
```

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

### Check ndtv Package Version

Source: https://statnet.org/nme/d3-s8-ndtv.R

Verify the installed version of the ndtv package. This should match the version number specified in the tutorial.

```R
library("ndtv")
packageVersion("ndtv")
```

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

### Initialize Network for Short Durations

Source: https://statnet.org/nme/d3-s8-ndtv.R

Initializes a network with 100 nodes and sets up parameters for a simulation with short edge durations (2 time steps).

```R
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 40
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 2)
est <- netest(nw, formation, target.stats, coef.diss)
```

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

### Browse NDTV Package Vignettes

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Open the package vignettes for NDTV to find more detailed examples and documentation. Vignettes provide in-depth guides on package usage.

```R
browseVignettes(package='ndtv')
```

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

### Load R Package

Source: https://statnet.org/nme/d0-tutR.html

Loads an installed package into the current R session, making its functions available for use.

```r
library("packagename")
```

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

### Fit ERGM with Valued Terms

Source: https://statnet.org/workshop-valued/valued.R

Example of fitting an ERGM model using valued network terms like mutuality and transitive/cyclical weights with a Binomial reference distribution. Ensure the 'ergm' package is installed and loaded.

```R
samplk.tot.ergm <- ergm(samplk.tot ~ sum + nonzero + mutual("min") + transitiveweights("min",
    "max", "min") + cyclicalweights("min", "max", "min"), reference = ~Binomial(3))
```

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

### Initialize Network and Run Basic CTERGM Simulation

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

Initializes an undirected network and runs a CTERGM simulation for 100 time units using the simEGP function. The coefficient is derived from the model's theoretical properties.

```R
set.seed(1331)
net <- network.initialize(10, directed = FALSE)  #Initialize the network
simbg <- simEGP(form = net ~ edges, coef = log(1.5/(9-1.5))/2, time = 100, 
    process = "CTERGM") #Run the simulation
```

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

### Install and Update Statnet Packages in R

Source: https://statnet.org/workshop-advanced-ergm/advanced_ergm_tutorial.R

Installs the 'ergm.multi' and 'ggrepel' packages, which will also install their dependencies like 'ergm' and 'network'. It then updates all installed packages to their latest versions.

```R
install.packages(c('ergm.multi', 'ggrepel'))
update.packages(ask=FALSE, checkBuilt=TRUE)
```

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

### Structure of Simulated Data

Source: https://statnet.org/workshop-relevent/sunbelt_2022_relevent_tutorial.html

This is an example of the output format for a simulated event sequence. It typically includes probabilities and event details.

```text
               [,1] [,2] [,3]
  [1,] 0.0004468037   27   13
  [2,] 0.0004626364   13   27
  [3,] 0.0004704296   27   13
  [4,] 0.0004819716   13   27
  [5,] 0.0004923628   27   13
  [6,] 0.0005274594   13   27
  [7,] 0.0005329692   27   13
  [8,] 0.0005379387   13   27
  [9,] 0.0005687593   27   13
 [10,] 0.0006085596   13   27
 [11,] 0.0006466945   27   13
 [12,] 0.0006974054   13   27
 [13,] 0.0007084274   27   13
 [14,] 0.0007339464   13   27
 [15,] 0.0007466830   27   13
 [16,] 0.0007492684   13   27
 [17,] 0.0007884910   27   13
 [18,] 0.0008027626   13   27
 [19,] 0.0008181866   27   13
 [20,] 0.0008316953   13   27
 [21,] 0.0008386961   27   13
 [22,] 0.0008486558   13   27
 [23,] 0.0008972992   27   13
 [24,] 0.0009089349   13   27
 [25,] 0.0009255863   27   13
 [26,] 0.0009356596   13   27
 [27,] 0.0009481630   27   13
 [28,] 0.0009701589   13   27
 [29,] 0.0010908132   27   13
 [30,] 0.0011515705   13   27
 [31,] 0.0011593256   27   13
 [32,] 0.0012039064   13   27
 [33,] 0.0012429321   27   13
 [34,] 0.0012671529   13   27
 [35,] 0.0012826756   27   13
 [36,] 0.0012830967   13   27
 [37,] 0.0012937788   27   13
 [38,] 0.0013168741   13   27
 [39,] 0.0013233227   27   13
 [40,] 0.0013545029   13   27
 [41,] 0.0013680710   27   13
 [42,] 0.0014074783   13   27
 [43,] 0.0014247736   27   13
 [44,] 0.0014539617   13   27
 [45,] 0.0015243130   27   13
 [46,] 0.0015514804   13   27
 [47,] 0.0015694450   27   13
 [48,] 0.0015723410   14    7
 [49,] 0.0016150099    7   14
 [50,] 0.0019005500   14    7
 [51,] 0.0019436756   14    1
 [52,] 0.0026720080   10   15
 [53,] 0.0027797172   15   27
 [54,] 0.0028284341   27   13
 [55,] 0.0028393713   13   27
 [56,] 0.0028778679   27   13
 [57,] 0.0028872755   13   27
 [58,] 0.0029061584   27   13
 [59,] 0.0029642798   13   27
 [60,] 0.0029984286   27   13
 [61,] 0.0030100941   13   27
 [62,] 0.0030511743   13   10
 [63,] 0.0031090747   10   13
 [64,] 0.0032220014   13   10
 [65,] 0.0032321834   10   13
 [66,] 0.0032814074   13   10
 [67,] 0.0032917877   10   13
 [68,] 0.0033707924   19   13
 [69,] 0.0034818303   13   19
 [70,] 0.0034824403   19   13
 [71,] 0.0034995468   13   19
 [72,] 0.0035038253   19   13
 [73,] 0.0036111933   13   19
 [74,] 0.0036167921   19   13
 [75,] 0.0036274954   13   19
 [76,] 0.0036565886   19   13
 [77,] 0.0037396416   13   19
 [78,] 0.0037581215   19   13
 [79,] 0.0037626692   13   19
 [80,] 0.0038189895   19   13
 [81,] 0.0039261296   13   19
 [82,] 0.0039629416   19   13
 [83,] 0.0040927937   13   19

```

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

### Network Animation Setup

Source: https://statnet.org/nme/d3-s8-ndtv.R

Retrieves the network from simulation results, applies color transformations, and defines parameters for computing and rendering a network animation.

```R
nw <- get_network(sim)
nw <- color_tea(nw)
slice.par <- list(start = 1, end = 25, interval = 1,
                  aggregate.dur = 1, rule = "any")
render.par <- list(tween.frames = 10, show.time = FALSE)
plot.par <- list(mar = c(0, 0, 0, 0))
compute.animation(nw, slice.par = slice.par, verbose = TRUE)
```

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

### Install ERGM Package

Source: https://statnet.org/workshop-ergm/ergm_tutorial.R

Install the 'ergm' package in R. This will also install the 'network' package if it is not already present.

```R
install.packages('ergm')
```

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

### Initialize Network with Triangles and Short Durations

Source: https://statnet.org/nme/d3-s8-ndtv.R

Initializes a network with 100 nodes, including triangle formation (gwesp) and short edge durations (20 time steps).

```R
nw <- network_initialize(n = 100)
formation <- ~edges + gwesp(0, TRUE)
target.stats <- c(40, 25)
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est <- netest(nw, formation, target.stats, coef.diss)
sim <- netsim(est, param, init, control)
```

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

### Initialize Network for Long Durations

Source: https://statnet.org/nme/d3-s8-ndtv.R

Initializes a network with 100 nodes and sets up parameters for a simulation with very long edge durations (1e5 time steps).

```R
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 40
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 1e5)
est <- netest(nw, formation, target.stats, coef.diss)
```

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

### Initialize Infected Individuals

Source: https://statnet.org/nme/d4-s3-Demography.R

Use `init.net` to specify the initial number of infected individuals in the simulation. This example sets 50 individuals to be infected initially.

```R
init <- init.net(i.num = 50)
```

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

### Install Statnet Packages in R

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

Installs the necessary Statnet packages for temporal network analysis in R. Ensure R and RStudio are installed first.

```r
# statnet packages
install.packages('tergm')
install.packages('tsna')
install.packages('ndtv')
```

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

### Initialize Network and Estimate Model Parameters

Source: https://statnet.org/nme/d3-s8-ndtv.R

Initialize a network with a specified number of nodes and define the formation model including 'edges' and 'concurrent' terms. Target statistics and dissolution coefficients are set for model estimation.

```R
nw <- network_initialize(n = 100)
formation <- ~edges + concurrent
target.stats <- c(40, 0)
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est <- netest(nw, formation, target.stats, coef.diss)
```

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

### Initialize Network and Set Formation Model

Source: https://statnet.org/nme/d3-s8-ndtv.R

Initialize a network with 100 nodes and define an edges-only formation model. Set target statistics and dissolution coefficients for network evolution.

```R
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 40
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est <- netest(nw, formation, target.stats, coef.diss)
```

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

### Dynamic Network Visualization with ndtv

Source: https://statnet.org/nme/d3.html

Tutorial and lab for visualizing dynamic networks using the ndtv package with EpiModel. Requires R.

```R
# R Code for Dynamic Network Visualization with ndtv
# (Specific code content not provided in the source text, only a link to R Code)
```

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

### Install Statnet Packages in R

Source: https://statnet.org/workshop-valued/valued.R

Installs essential Statnet packages for network analysis, including ergm.count, ergm.rank, and latentnet. It also updates all installed packages to their latest versions.

```r
install.packages("ergm.count")
install.packages("ergm.rank")
install.packages("latentnet")
update.packages()
```

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

### Dynamic Network Initialization and Parameterization

Source: https://statnet.org/nme/d2-s8-TERGMs.R

Initialize a network with a dynamic number of nodes and calculate target statistics as a function of network size and desired mean degree.

```R
# Do not run
nnodes <- 1000
meandeg <- 0.4

net2 <- network_initialize(nnodes)
fit2 <- netest(net2, 
   formation = ~edges, 
   target.stats = nnodes*meandeg/2,
   coef.diss = dissolution_coefs(~offset(edges), 90))
```

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

### Configure Network Simulation Control Settings

Source: https://statnet.org/nme/d4-s3-Demography.R

Use `control.net` to set simulation parameters. Key arguments include `resimulate.network` to enable dynamic network updates, `epi.by` for stratified prevalence, and `tergmLite` for simulation efficiency.

```R
control.full <- control.net(type = "SI", nsteps = 300, nsims = 1, ncores = 1, 
                            resimulate.network = TRUE, epi.by = "risk", tergmLite = FALSE)
```

```R
control.tl <- control.net(type = "SI", nsteps = 300, nsims = 1, ncores = 1, 
                          resimulate.network = TRUE, epi.by = "risk", tergmLite = TRUE)
```

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

### Initialize Network

Source: https://statnet.org/nme/d2-s8-TERGMs.R

Initialize a dynamic network with a specified number of nodes. This creates an empty network structure.

```r
net1 <- network_initialize(100)
```

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

### Initialize a Static Network

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Initializes a static network with a specified number of vertices. Use this to begin creating a network structure.

```R
wheel <- network.initialize(10)
class(wheel)
```

```R
[1] "network"
```

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

### Check 'relevent' Package Version

Source: https://statnet.org/workshop-relevent-experiments/in_silico_experiments_tutorial.RData

Displays the installed version number of the 'relevent' package. Useful for verifying installation.

```R
packageVersion("relevent")
```

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

### Initialize and Simulate Network with Modified Size

Source: https://statnet.org/nme/d2-s8-TERGMs.R

Initialize a network with a specified number of nodes and simulate its evolution. Adjust target statistics when changing network size.

```R
net2 <- network_initialize(1000)
fit2 <- netest(net2, 
   formation = ~edges, 
   target.stats = 200,
   coef.diss = dissolution_coefs(~offset(edges), 90))
sim2 <- netdx(fit2, nsteps = 1000, nsims = 10, keep.tedgelist = TRUE)
```

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

### Simulation Initialization Output

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

This output shows the initialization process for each simulated trajectory, including the maximum number of events, time limits, and initial potential values.

```text
Initializing simulation: max events=6, max time=inf, initial pot=(0.000000,0.000000)
event=0, t=0.000000, pot=(0.000000,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(-6.614965,0.000000)
event=0, t=0.000000, pot=(-6.614965,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(-6.068909,0.000000)
event=0, t=0.000000, pot=(-6.068909,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(-7.205523,0.000000)
event=0, t=0.000000, pot=(-7.205523,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(1.308488,0.000000)
event=0, t=0.000000, pot=(1.308488,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(4.462090,0.000000)
event=0, t=0.000000, pot=(4.462090,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(10.371563,0.000000)
event=0, t=0.000000, pot=(10.371563,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(9.941063,0.000000)
event=0, t=0.000000, pot=(9.941063,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(16.868028,0.000000)
event=0, t=0.000000, pot=(16.868028,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(28.695987,0.000000)
event=0, t=0.000000, pot=(28.695987,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(30.802444,0.000000)
event=0, t=0.000000, pot=(30.802444,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(33.829234,0.000000)
event=0, t=0.000000, pot=(33.829234,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(39.418635,0.000000)
event=0, t=0.000000, pot=(39.418635,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(42.200572,0.000000)
event=0, t=0.000000, pot=(42.200572,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(47.073620,0.000000)
event=0, t=0.000000, pot=(47.073620,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(51.472271,0.000000)
event=0, t=0.000000, pot=(51.472271,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(52.581093,0.000000)
event=0, t=0.000000, pot=(52.581093,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(56.297169,0.000000)
event=0, t=0.000000, pot=(56.297169,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(59.387128,0.000000)
event=0, t=0.000000, pot=(59.387128,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(64.073258,0.000000)
event=0, t=0.000000, pot=(64.073258,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(66.237036,0.000000)
event=0, t=0.000000, pot=(66.237036,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(72.609808,0.000000)
event=0, t=0.000000, pot=(72.609808,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(80.081198,0.000000)
event=0, t=0.000000, pot=(80.081198,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(77.794111,0.000000)
event=0, t=0.000000, pot=(77.794111,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(72.354091,0.000000)
event=0, t=0.000000, pot=(72.354091,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(0.000000,0.000000)
event=0, t=0.000000, pot=(0.000000,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(3.649974,0.000000)
event=0, t=0.000000, pot=(3.649974,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(14.044389,0.000000)
event=0, t=0.000000, pot=(14.044389,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(16.614915,0.000000)
event=0, t=0.000000, pot=(16.614915,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(22.589453,0.000000)
event=0, t=0.000000, pot=(22.589453,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(23.076663,0.000000)
event=0, t=0.000000, pot=(23.076663,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(28.597234,0.000000)
event=0, t=0.000000, pot=(28.597234,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(31.908205,0.000000)
event=0, t=0.000000, pot=(31.908205,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(26.006400,0.000000)
event=0, t=0.000000, pot=(26.006400,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(27.798922,0.000000)
event=0, t=0.000000, pot=(27.798922,0.000000)
Initializing simulation: max events=6, max time=inf, initial pot=(39.877427,0.000000)

```

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

### Install ergm.ego from CRAN

Source: https://statnet.org/workshop-ergm-ego/ergm.ego_tutorial.R

Use this command to install the ergm.ego package and all its required dependencies from the Comprehensive R Archive Network (CRAN).

```r
install.packages('ergm.ego')
```

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

### Initialize and Run Network Simulation in R

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Loads network data, sets simulation parameters like time step and transmission probability, initializes the rumor spread from a starting vertex, and then runs the simulation using the previously defined `runSim` function. Ensure the `networkDynamic` data object is loaded before executing.

```R
data(windsurfers)    # let's go to the beach!

# set initial params...
timeStep <- 1  # units are in days
transProb <- 0.2 # how likely to tell in each conversation/day
# start the rumor out on vertex 1
activate.vertex.attribute(windsurfers,"knowsRumor",TRUE,v=1,
                          onset=0-timeStep,terminus=Inf)
activate.vertex.attribute(windsurfers,"heardRumorFrom",1,v=1,
                          onset=0-timeStep,length=timeStep)
activate.edge.attribute(windsurfers,'passedRumor',value=FALSE,
onset=-Inf,terminus=Inf)
# run the sim!
windsurfers<-runSim(windsurfers,timeStep,transProb)
```

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

### Access R Help Documentation

Source: https://statnet.org/nme/d0-tutR.html

Retrieves help documentation for a specific function or package in R. Use '?' or help().

```r
?mean
```

```r
help("mean")
```

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

### Check R Library Path

Source: https://statnet.org/install-statnet

This command displays the location of your R library where packages are installed. Useful for verifying installation paths.

```r
>  .libPaths()

```

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

### Initialize Network Model

Source: https://statnet.org/nme/d4-s5-Serosort.R

Initializes a network model with a specified number of nodes. Use this to begin setting up network simulations.

```R
n <- 500
nw <- network_initialize(n)
```

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

### Verify ergm.ego Package Version

Source: https://statnet.org/workshop-ergm-ego/ergm.ego_tutorial.R

Check the installed version of the ergm.ego package. This is useful for confirming a successful installation or ensuring compatibility with specific project requirements.

```r
packageVersion('ergm.ego')
```

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

### Initialize Network and Set Up SIR Model Parameters

Source: https://statnet.org/nme/d4-s9-SEIR.R

This snippet initializes a network, defines formation and dissolution coefficients, sets up model parameters, initial conditions, and control settings for an SIR simulation. It is a foundational step for running network-based epidemic simulations with EpiModel.

```r
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 50
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est1 <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)
param <- param.net(inf.prob = 0.3, rec.rate = 0.1)
init <- init.net(i.num = 10, r.num = 0)
control <- control.net(type = "SIR", nsteps = 25, nsims = 1, verbose = FALSE)
mod1 <- netsim(est1, param, init, control)
```

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

### Attach required packages

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

Loads the 'tergm', 'tsna', 'ndtv', 'htmlwidgets', and 'latticeExtra' packages into the current R session.

```R
library(tergm)
library(tsna)
library(ndtv)
library(htmlwidgets)
library(latticeExtra)
```

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

### Initialize Network and Assign Attributes

Source: https://statnet.org/workshop-advanced-ergm/advanced_ergm_tutorial.R

Initializes an empty network of a specified size and assigns nodal attributes based on provided weights. This step prepares the network structure before fitting the ERGM.

```R
set.seed(4321)
net_size <- 50000
nw <- network.initialize(net_size, directed = FALSE)
inds <- sample(seq_len(NROW(cohab_PopWts)), 
               net_size, TRUE, cohab_PopWts$weight)
set.vertex.attribute(nw, names(cohab_PopWts)[-1], 
                     cohab_PopWts[inds,-1])
```

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

### Prepare Multilayer Network with Layer() (Method 2)

Source: https://statnet.org/workshop-advanced-ergm/advanced_ergm_tutorial.R

Create a multilayer network object by passing networks as named arguments to the Layer() function. This is a convenient way to combine networks when they are readily available.

```R
# Method 2: networks as arguments
flo <- Layer(m = flomarriage, b = flobusiness)
```

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

### Explore egor Object Example in R

Source: https://statnet.org/workshop-ergm-ego/ergm.ego_tutorial.R

Run the example for the 'egor' function to see a demonstration of its usage with alter-alter ties. This is useful for understanding more complex scenarios.

```r
example("egor")
```

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

### Configure Network Simulation Control Settings

Source: https://statnet.org/nme/d4-s5-Serosort.R

Sets up control parameters for the simulation, including simulation type, number of steps and simulations, core usage, network resimulation, and monitoring statistics. Use 'tergmLite = TRUE' when individual-level network histories are not required.

```R
control <- control.net(type = "SI", nsteps = 500, nsims = 5, ncores = 5,
                       resimulate.network = TRUE, tergmLite = TRUE,
                       nwstats.formula = ~edges +
                                          meandeg +
                                          nodefactor("status", levels = NULL) +
                                          nodematch("status"))
```

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

### Load Workshop Data in R

Source: https://statnet.org/workshop-relevent-experiments/in_silico_experiments_tutorial.RData

Loads the 'in_silico_experiments_tutorial.RData' file. Ensure the data file is in your current working directory or provide the correct path.

```R
load("in_silico_experiments_tutorial.RData")
```

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

### Install and Load tsna Package

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

Installs the tsna package and its dependencies from CRAN, then loads it. This package is used for temporal SNA and will also load the sna and ergm packages.

```r
install.packages('tsna',repos='http://cran.us.r-project.org', 
                 dependencies=TRUE)
library(tsna)
```

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

### Install 'graph' Package from Bioconductor

Source: https://statnet.org/workshop-intro-sna-tools

Installs the 'graph' package using the 'remotes' package, which is necessary for certain network analysis functionalities. This command is specific to Bioconductor repositories.

```r
remotes::install_bioc("graph")
```

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

### Initialize and Animate MDSJ Layout

Source: https://statnet.org/workshop-ndtv/ndtv_workshop.html

This snippet initializes a network and then animates it using the MDSJ layout algorithm. This action may trigger a prompt to download the MDSJ Java library if it's not already installed.

```r
network.layout.animate.MDSJ(network.initialize(1))
```

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

### Install ergm.ego from R-universe

Source: https://statnet.org/workshop-ergm-ego/ergm.ego_tutorial.R

Install the development version of ergm.ego from Statnet's R-universe repository, which may include newer features or bug fixes not yet available on CRAN.

```r
install.packages(
  "ergm.ego", 
  repos = c("https://statnet.r-universe.dev", "https://cloud.r-project.org")
)
```

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

### Initialize Network for Simulation

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

Creates a base network for simulation using `rgraph` and assigns random memberships to nodes. Ensure `set.seed` is used for reproducibility.

```R
set.seed(1331)
net <- network(rgraph(50, tp=1.5/49, mode="graph"), directed = FALSE)
net %v% "x" <- sample(0:1, 50, replace = TRUE)
```

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

### ERGMP: Starting Maximum Pseudolikelihood Estimation (MPLE)

Source: https://statnet.org/workshop-ergmgp/ergmgp_tutorial.R

This output indicates the start of the Maximum Pseudolikelihood Estimation (MPLE) process for the ERGM model. It is a computational step in fitting the model.

```text
Starting maximum pseudolikelihood estimation (MPLE):
```

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

### Initialize Network Conditions

Source: https://statnet.org/nme/d4-s5-Serosort.R

Creates an empty object for initial network conditions. The actual initial conditions are passed through the 'netest' object.

```R
init <- init.net()
```

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

### Create a Dynamic Network with a Specified Start Time

Source: https://statnet.org/workshop-tergm/tergm_tutorial.R

Creates a networkDynamic object from a list of networks, explicitly setting the starting time to 1. This adjusts the temporal indexing of the network data.

```r
samp.dyn <- networkDynamic(network.list = samp.list, start=1)
```

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

### Specify Initial Conditions for Epidemic Model

Source: https://statnet.org/nme/d4-s9-SEIR.R

Initializes the epidemic model by setting the number of infectious individuals at the start. For models starting with latent individuals, nodal attributes on the network should be used.

```R
init <- init.net(i.num = 10)
```

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

### Plot Relational Durations

Source: https://statnet.org/nme/d2-s8-TERGMs.R

Visualizes the relational durations within the simulated network. Set duration.imputed to FALSE to exclude imputed starting dates for ties present at the simulation's start.

```R
plot(sim1, type = "duration")
```

```R
plot(sim1, type = "duration", duration.imputed = FALSE)
```

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

### Example: Displaying Newcomb's Fraternity Data

Source: https://statnet.org/workshop-valued/valued.R

This example demonstrates how to load and display Newcomb's fraternity data as a matrix with rank attributes. It is useful for practicing with ordinal network data.

```R
data(newcomb)
as.matrix(newcomb[[1]], attrname = "rank")
```

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

### View ERGM Term Cross-Reference Guide

Source: https://statnet.org/workshop-ergm/ergm_tutorial.R

Access the cross-reference guide vignette to find tables summarizing keywords for terms, definitions, and a list of keywords with their corresponding terms. This provides a comprehensive overview.

```r
vignette("ergm-term-crossRef")
```

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

### Get Structure of an R Object

Source: https://statnet.org/nme/d0-tutR.html

Use the `str()` function to get a concise summary of the internal structure of an R object, including data frames. Useful for understanding data types and dimensions.

```R
str(h)
```

```R
'data.frame':   10 obs. of  2 variables:
 $ id : int  1 2 3 4 5 6 7 8 9 10
 $ sex: chr  "Female" "Female" "Female" "Female" ...

```

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

### Prepare Multilayer Network with Layer() (Method 1)

Source: https://statnet.org/workshop-advanced-ergm/advanced_ergm_tutorial.R

Create a multilayer network object by passing a list of networks to the Layer() function. This method is useful when networks are already stored as a list.

```R
data(florentine)

# Method 1: list of networks
flo <- Layer(list(m = flomarriage, b = flobusiness))
```

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

### Initialize Network Parameters

Source: https://statnet.org/nme/d5-s6-COVIDScreen.R

Sets up age-specific mortality rates and age spans for network initialization. Mortality rates are converted to a daily probability.

```R
ages <- 0:85
departure_rate <- c(588.45, 24.8, 11.7, 14.55, 47.85, 88.2, 105.65, 127.2,
                    154.3, 206.5, 309.3, 495.1, 736.85, 1051.15, 1483.45,
                    2294.15, 3642.95, 6139.4, 13938.3)
dr_pp_pd <- departure_rate / 1e5 / 365
age_spans <- c(1, 4, rep(5, 16), 1)
dr_vec <- rep(dr_pp_pd, times = age_spans)
```

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

### Set up Model Skeleton with Time-Varying Covariates

Source: https://statnet.org/workshop-relevent/relevent_tutorial.html

This code initializes a time-varying covariate and sets up the model skeleton. Note that covariates must be passed as static when creating the skeleton, as the model does not yet contain time points.

```R
set.seed(1331)
#Set up the model
tcovar<-array(sweep(sapply(1:10,rep,100),1,1/1.05^(0:99),"*"),dim=c(100,10,1))
SndInt<-rep(1,10)
#Note that, in making the skeleton, we need to pass the covariates as if
#they are static - that's because the model doesn't contain time points yet.
modskel2<-rem.dyad(NULL, n=10, effects=c("CovSnd", "CovInt"), coef.seed=c(-1,1), covar=list(CovSnd=SndInt, CovInt=tcovar[1,,1]))
```

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

### Calculate Network Metrics (Second Example)

Source: https://statnet.org/COVID-JustOneFriend

Calculates network metrics for the second combined network, similar to the first example. Use this to compare structural properties of networks generated with different ERGM specifications.

```R
largcomp.comb.2 <- sum(component.largest(net.comb.2))
kpath.comb.2 <- sna:::kpath.census(net.comb.2)$path.count[3,'Agg']/2
```