Module overview

Recall that SpaDES simulations are event-driven, meaning that different actions are performed on data objects based on the order of scheduled events. The central design of SpaDES promotes modularity, such that collections of related simulation actions can be grouped together as ‘modules’ and easily reused among multiple simulations. Strict modularity requires that modules can act independently, without needing to know about other modules. Thus each SpaDES module must explicitly state its input dependencies (data, package, and parameterization requirements), data outputs, as well as provide other useful metadata and documentation for the user. Upon initialization of a simulation via simInit, the dependencies of every module used are examined and evaluated. If dependency incompatibilities exists, the initialization fails and the user is notified. Also during this initialization, module code is source-ed into the simulation environment, making all module objects and functions available during the simulation.

Each SpaDES module describes the processes or activities that drive simulation state changes via changes to objects stored in the simulation environment. Each activity consists of a collection of events which are scheduled depending on the rules of the simulation. Each event may evaluate or modify a simulation data object (e.g., update the values on a raster map), or perform other operations such as saving and loading data objects, plotting, or scheduling other events.

Module directory structure (moduleName/)

A module consists of a collection of files and folders that …

/moduleRepository
  |_ moduleName/
      |_ R/                     # contains additional module R scripts
      |_ data/                  # directory for all included data
          |_ CHECKSUMS.txt      # contains checksums for data files
      |_ tests/                 # contains unit tests for module code
      |_ citation.bib           # bibtex citation for the module
      |_ LICENSE.md             # describes module's legal usage
      |_ NEWS.md                # log of changes made to module code
      |_ README.md              # symlink to rendered documentation file (moduleName.md)
      |_ moduleName.R           # module code file (incl. metadata)
      |_ moduleName.Rmd         # documentation, usage info, etc.
      |_ moduleName_x.y.z.zip   # zip archive of previous versions

Module code file (moduleName.R)

A SpaDES module consists of a single .R source file, whose name matches the name of the module. This file consists of three parts containing the code for:

1. the metadata describing the module and its data dependencies;
2. defining the event types described in the module;
3. functions describing what happens during the processing of each event type.

## sample module metadata for the default `caribouMovement` module
defineModule(sim, list(
  name = "caribouMovement",
  description = "Simulate caribou movement via correlated random walk.",
  keywords = c("caribou", "individual based movement model", "correlated random walk"),
  childModules = character(),
  authors = c(person(c("Eliot", "J", "B"), "McIntire", email = "[email protected]",
                     role = c("aut", "cre"))),
  version = list(caribouMovement = "1.6.1"),
  spatialExtent = terra::ext(rep(0, 4)),
  timeframe = as.POSIXlt(c(NA, NA)),
  timeunit = "month",
  citation = list(),
  documentation = list(),
  reqdPkgs = list("grid", "terra", "sf", "stats", "SpaDES.tools (>= 2.0.0)"),
  parameters = rbind(
    defineParameter("stackName", "character", "landscape", NA, NA, "name of the RasterStack"),
    defineParameter("moveInitialTime", "numeric", start(sim) + 1, start(sim) + 1, end(sim),
                    "time to schedule first movement event"),
    defineParameter("moveInterval", "numeric", 1.0, 1, 1,
                    "time interval between movoment events"),
    defineParameter("N", "numeric", 100L, 10L, 1000L, "initial number of caribou"),
    defineParameter("torus", "logical", FALSE, FALSE, TRUE,
                    "should the map wrap around like a torus?"),
    defineParameter(".plots", "character", "screen", NA, NA,
                    "A modular mechanism to create plots, using png, screen device or other. See `?Plots`."),
    defineParameter(".plotInitialTime", "numeric", start(sim), -Inf, Inf,
                    "time to schedule first plot event"),
    defineParameter(".plotInterval", "numeric", 1, -Inf, Inf,
                    "time interval between plot events"),
    defineParameter(".saveInitialTime", "numeric", NA_real_, -Inf, Inf,
                    "time to schedule first save event"),
    defineParameter(".saveInterval", "numeric", NA_real_, -Inf, Inf,
                    "time interval between save events"),
    defineParameter(".seed", "list", list(), NA, NA,
                    paste("Named list of seeds to use for each event (names).",
                          "E.g., `list('init' = 123)` will `set.seed(123)`",

  library(SpaDES.core)

## Loading required package: quickPlot

## Loading required package: reproducible

## 
## Attaching package: 'SpaDES.core'

## The following objects are masked from 'package:stats':
## 
##     end, start

## The following object is masked from 'package:utils':
## 
##     citation

outputDir <- file.path(tempdir(), "simOutputs")
times <- list(start = 0.0, end = 5.0)
parameters <- list(
  .globals = list(stackName = "landscape", burnStats = "nPixelsBurned"),
  .progress = list(NA),
  randomLandscapes = list(nx = 100L, ny = 100L, inRAM = TRUE),
  fireSpread = list(
    nFires = 10L, spreadprob = 0.225, its = 1e6, persistprob = 0,
    returnInterval = 10, startTime = 0,
    .plotInitialTime = 0, .plotInterval = 10
  ),
  caribouMovement = list(
    N = 100L, moveInterval = 1, torus = TRUE,
    .plotInitialTime = 1, .plotInterval = 1
  )
)
modules <- list("randomLandscapes", "fireSpread", "caribouMovement")
objects <- list()
paths <- list(modulePath = getSampleModules(tempdir()),
              outputPath = outputDir)

mySim <- simInit(times = times, params = parameters, modules = modules,
                 objects = objects, paths = paths)

## Setting:
##   options(
##     spades.outputPath = '/tmp/RtmpXsEdc9/simOutputs'
##     spades.modulePath = '/tmp/RtmpXsEdc9/sampleModules'
##   )

## Paths set to:
##   options(
##     rasterTmpDir = '/tmp/RtmpXsEdc9/SpaDES/scratch/raster'
##     reproducible.cachePath = '/tmp/RtmpXsEdc9/reproducible/cache'
##     spades.inputPath = '/tmp/RtmpXsEdc9/SpaDES/inputs'
##     spades.outputPath = '/tmp/RtmpXsEdc9/simOutputs'
##     spades.modulePath = '/tmp/RtmpXsEdc9/sampleModules'
##     spades.scratchPath = '/tmp/RtmpXsEdc9/SpaDES/scratch'
##   )
##   terra::terraOptions(tempdir = '/tmp/RtmpXsEdc9/SpaDES/scratch/terra'

## Loading required package: RColorBrewer

## Loading required package: terra

## terra 1.7.78

## 
## Attaching package: 'terra'

## The following object is masked from 'package:SpaDES.core':
## 
##     time<-

## The following object is masked from 'package:MASS':
## 
##     area

## The following object is masked from 'package:SpaDES.tools':
## 
##     wrap

## The following object is masked from 'package:knitr':
## 
##     spin

## Loading required package: grid

## 
## Attaching package: 'grid'

## The following object is masked from 'package:terra':
## 
##     depth

## Loading required package: sf

## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE

## Oct13 23:29:46 simInit Using setDTthreads(1). To change: 'options(spades.DTthreads = X)'.

## Oct13 23:29:47 simInit The following .globals were used:

## Oct13 23:29:47 simInit Key: <global, module>
## Oct13 23:29:47 simInit module    global
## Oct13 23:29:47 simInit <char>    <char>
## Oct13 23:29:47 simInit 1:       fireSpread burnStats
## Oct13 23:29:47 simInit 2:  caribouMovement stackName
## Oct13 23:29:47 simInit 3:       fireSpread stackName
## Oct13 23:29:47 simInit 4: randomLandscapes stackName

## Elapsed time for simInit: 0.5251901 secs

## Access parameters
P(mySim)                    # shows all parameters
P(mySim, module = "caribouMovement") # only parameters in caribouMovement module
P(mySim)$caribouMovement    # same
P(mySim)$caribouMovement$N  # Only one parameter

## If used within the module source code, then module name can be omitted:
## This will return NULL here, but will return the actual value if used
## in a module
P(mySim)$N  # Only one parameter if used within a module

## sample event type definitions for the default `caribouMovement` module
  )
))

## event types
doEvent.caribouMovement <- function(sim, eventTime, eventType, debug = FALSE) {
  switch(
    eventType,
    init = {
      ### check for more detailed object dependencies:
      ### (use `checkObject` or similar)
      checkObject(sim, name = Par$stackName, layer = "habitatQuality")

      # do stuff for this event
      sim <- Init(sim)

      # schedule the next event
      sim <- scheduleEvent(sim, Par$moveInitialTime,
                           "caribouMovement", "move")
      sim <- scheduleEvent(sim, Par$.plotInitialTime,
                           "caribouMovement", "plot.init", .last())
      sim <- scheduleEvent(sim, Par$.saveInitialTime,
                           "caribouMovement", "save", .last() + 1)
    },
    move = {
      # do stuff for this event
      sim <- Move(sim)

      # schedule the next event
      sim <- scheduleEvent(sim, time(sim) + Par$moveInterval, "caribouMovement", "move")
    },
    plot.init = {
      # If this is scheduled because .plotInitialTime is not NA, it may not do anything
      #   if Par$.plots is not "screen", "png" or another type.
      Plots(sim$caribou, addTo = paste("sim", Par$stackName, "habitatQuality", sep = "$"),
           new = FALSE, size = 0.2, pch = 19, gp = gpar(cex = 0.6), usePlot = TRUE)

      # schedule the next event
      sim <- scheduleEvent(sim, time(sim) + Par$.plotInterval, "caribouMovement", "plot", .last())
    },
    plot = {
      Plots(sim$caribou, new = FALSE, pch = 19, size = 0.1, gp = gpar(cex = 0.6),
            usePlot = TRUE)

      # schedule the next event
      sim <- scheduleEvent(sim, time(sim) + Par$.plotInterval, "caribouMovement", "plot", .last())
    },
    save = {
      # do stuff for this event
      sim <- saveFiles(sim)

      # schedule the next event
      sim <- scheduleEvent(sim, time(sim) + Par$.saveInterval, "caribouMovement", "save", .last() + 1)

    },
    warning(paste(
      "Undefined event type: \'", events(sim)[1, "eventType", with = FALSE],

## sample Init event function from the default `caribouMovement` module
}

## event functions
Init <- function(sim) {
  yrange <- c(ymin(sim[[Par$stackName]]),
              ymax(sim[[Par$stackName]]))
  xrange <- c(xmin(sim[[Par$stackName]]),
              xmax(sim[[Par$stackName]]))

  # initialize caribou agents
  N <- Par$N
  IDs <- as.character(1:N)
  sex <- sample(c("female", "male"), N, replace = TRUE)
  age <- round(rnorm(N, mean = 8, sd = 3))
  x1 <- rep(0, N)
  y1 <- rep(0, N)
  starts <- cbind(x = runif(N, xrange[1], xrange[2]),
                  y = runif(N, yrange[1], yrange[2]))

  # create the caribou agent object
  sim$caribou <- vect(cbind(starts, data.frame(IDs, x1, y1, sex, age)), geom = c("x", "y"))

  return(invisible(sim))

## png 
##   2

Module documentation (moduleName.Rmd)

Module documentation should be written so that others can use your module. As such, this documentation should include a high-level overview of the module as well as low-level, detailed descriptions of its usage.

We strongly recommend using R Markdown (.Rmd) for this documentation, as it is easily human-readable while also being machine-readable and easily converted to a wide variety of file formats including PDF, HTML, and DOCX. Another benefit of using a text-based file format is to facilitate transparent document versioning and history using version control systems like git. For help writing in R Markdown, see https://rmarkdown.rstudio.com/.

Citation, LICENSE, and README files

• citation.bib provides a BibTeX formatted citation with which the module can be referenced. Most citation management software can read and output .bib format.

• LICENSE.txt describes how your module can be redistributed and used by others. For help selecting a license, see choosealicense.com.

• README.md is used to briefly document module metadata and version change information.

Data

All module data are located in the module’s data/ subdirectory. Also included in this folder is a CHECKSUMS.txt file that contains the expected checksums for each data file. Checksums can be verified (and optionally written) using checksums().

## 1. specify your module here
moduleName <- "my_module"

## 2. use a temp dir to ensure all modules get fresh copies of the data
tmpdir <- file.path(tempdir(), "SpaDES_modules")

## 3. download your module's data to the temp dir
downloadData(moduleName, tmpdir)

## 4. initialize a dummy simulation to ensure any 'data prep' steps in the .inputObjects section are run
simInit(modules = moduleName)

## 5. recalculate your checksums and overwrite the file
checksums(moduleName, tmpdir, write = TRUE)

## 6. copy the new checksums file to your working module directory (the one not in the temp dir)
file.copy(from = file.path(tmpdir, moduleName, 'data', 'CHECKSUMS.txt'),
          to = file.path('path/to/my/moduleDir', moduleName, 'data', 'CHECKSUMS.txt'),
          overwrite = TRUE)

Modules may require data that for various reasons cannot be distributed with the module source code. In these cases, the module developer should ensure that the module downloads and extracts the data required. It is useful to not only check that the data files exist locally but that their checksums match those expected (see checksums()).

Versioned zip archives (moduleName_x.y.z.zip)

Use semantic versioning, such that a version number x.y.z corresponds to:

• x is the major version (incremented when you make incompatible API changes)
• y is the minor version (incremented when you add functionality in a backwards-compatible manner)
• z is the patch version (incremented when you make backwards-compatible bug fixes)

Data types

As you build your modules for your simulation, you can use any of R’s data types to store your objects and data. In particular, matrices (including vectors) and lists work well for this purpose because, as of R version 3.1.0, they are more efficient, reducing your model’s memory footprint and speeding up your code’s execution. Other useful data types include Raster* and SpatialPoints* objects (see examples below).

Global objects and the simulation environment

R passes copies of objects to functions (instead of using pass-by-reference), so the typical R function never acts directly on the global copy of the variable. Within a function, all operations are generally performed on a local copy of the object, which is then either discarded or returned back to the parent environment upon when the function completes. This object-copying behaviour has given R a reputation of being slow in comparison to languages like C and FORTRAN. However, recent improvements to R have made it more memory efficient and faster to execute, in part by minimizing the number of object copies made and lazy loading.

One way SpaDES reduces object copying is by storing and modifying objects in a simulation environment, because environments in R have reference semantics. Instead of storing simulation data in the global environment, each SpaDES simulation uses a unique environment wherein simulation data are referenced.

Module object dependencies

As noted above, modules should not depend on one another for event scheduling. However, it is often useful to develop collections of modules that interact indirectly and are dependent on shared data objects. Modules can be designed to rely on outputs (data objects) from other modules. When objects are shared between modules, it is important to state these dependencies explicitly. To check whether objects upon which a model depends exist, a call to checkObject is made during module initialization.

Note that modules need not be inter-dependent on one another: module B may depend on module A (for example to initialize a data object), without module A depending on module B.

To view the dependencies for a simulation:

## NOTE: Suggested packages SpaDES.tools and NLMR packages must be installed
#install.packages("SpaDES.taols")
#install.packages("NLMR", repos = "https://predictiveecology.r-universe.dev/")

library(SpaDES.core)

times <- list(start = 0.0, end = 1.0)
parameters <- list(
  .globals = list(stackName = "landscape", burnStats = "nPixelsBurned")
)
modules <- list("SpaDES_sampleModules")
paths <- list(modulePath = getSampleModules(tempdir()))

mySim <- simInit(times = times, params = parameters, modules = modules, paths = paths)

## examine simulation module (object) dependencies
depsEdgeList(mySim, FALSE)      # all object dependency relationships
clearPlot()

moduleDiagram(mySim)            # simplified visual representation of modules

clearPlot()

moduleDiagram(mySim, showParents = TRUE) # similar, but showing parent module grouping

# detailed visual representation of objects
objectDiagram(mySim, width = 720)

The output of the depsEdgeList is an edgelist describing the relationships between each module. In the example above, line 2 of the output indicates that the fireSpread module relies on an object landscape provided by the caribouMovement module. This edgelist is used inside the depsGraph function to produce a dependency graph (network), where each module corresponds to a vertex (node), and object dependencies are denoted by the arrows. When plotting the dependency graph, it’s often useful to use the plot=TRUE argument to simplify the network by showing no more than one object dependency between modules.

.checkpoint module

Schedule automatic simulation checkpointing to allow you to resume a previously saved simulation. All objects in the global environment including the state of the random number generator are saved and can be reloaded. Checkpoint frequency and filename can be passed as parameters to the simulation object as illustrated below. By default, checkpointing is not used unless the interval parameter is provided; the simulation checkpoint file (if one exists) is loaded if the file parameter is specified.

# initialize a new simulation, setting the checkpoint interval and filename.
times <- list(start = 0, end = 5)
parameters <- list(
  .globals = list(stackName = "landscape"),
  .checkpoint = list(interval = 2, file = "chkpnt.RData")
)
modules <- list("randomLandscapes", "caribouMovement")
paths <- list(
  modulePath = getSampleModules(tempdir())
)

mySim <- simInit(times = times, params = parameters, modules = modules, paths = paths)

# retrieve the checkpoint params from the simulation object
checkpointFile(mySim)

## NULL

checkpointInterval(mySim)

## [1] NA

.progress module

Schedule updates to the simulation progress bar.

# initialize a new simulation, setting the progress parameters
mySim <- simInit(times = list(start = 0.0, end = 10.0),
                 params = list(.globals = list(stackName = "landscape"),
                               .progress = list(type = "text", interval = 1)),
                 modules = list("randomLandscapes"),
                 paths = list(modulePath = getSampleModules(tempdir())))

# retrieve the checkpoint params from the simulation object
progressType(mySim)

## [1] "text"

progressInterval(mySim)

## [1] 1

.load and .save modules

Schedule object save and file load events by passing parameters to the save and load parameters for each module. Saving objects to file can be done at the module level or at the model level. At the module level, each module can be provided with appropriate arguments as parameters. In all cases, these must be events that are scheduled in the modules that do the saving or loading, and then the parameters necessary to make those events happen must be specified. Modules are not required to have the following parameters set, but when using newModule() to create the module, these will be events that are made, but not scheduled:

# initialize a new simulation, setting the load and save parameters
outputDir <- file.path(tempdir(), "simOutputs")
mySim <- simInit(times = list(start = 0, end = 5),
                 params = list(
                   .globals = list(stackName = "landscape"),
                   randomLandscapes = list(
                     .saveInitialTime = 0, .saveInterval = 2,
                     .saveObjects = c("landscape"),
                     .savePath = file.path(outputDir, "randomLandscapes"))
                   ),
                 modules = list("randomLandscapes"),
                 paths = list(modulePath = getSampleModules(tempdir()),
                              outputPath = outputDir),
                 outputs = data.frame(objectName = "landscape")
)

# retrieve the load and save params from the simulation object
inputs(mySim)    # shows all files and objects that are "inputs"

## [1] file       fun        package    objectName loadTime   loaded     arguments 
## [8] intervals 
## <0 rows> (or 0-length row.names)

outputs(mySim)   # shows all files and objects that are "outputs"

##   objectName                                           file     fun package
## 1  landscape /tmp/RtmpXsEdc9/simOutputs/landscape_year5.rds saveRDS    base
##   saveTime saved arguments
## 1        5    NA        NA

params(mySim)$randomLandscapes$.saveInitialTime

## [1] 0

params(mySim)$randomLandscapes$.saveInterval

## [1] 2

params(mySim)$randomLandscapes$.saveObjects

## [1] "landscape"

params(mySim)$randomLandscapes$.savePath

## [1] "/tmp/RtmpXsEdc9/simOutputs/randomLandscapes"

ftmp <- tempfile(pattern = "spades_vignetteOutputs", fileext = ".pdf")
pdf(ftmp)
clearPlot()
mySim2 <- spades(mySim)

# More sophisticated, passing arguments to outputs()
outputs(mySim) <- data.frame(
  objectName = "landscape", fun = "writeRaster", package = "terra",
  saveTime = c(3, 6), arguments = I(lapply(c(3, 6), function(x) {
    list(datatype = "FLT4S", filetype = "GTiff", overwrite = TRUE)
})))
mySim2 <- spades(mySim)

## Warning in x@ptr$writeRaster(opt): GDAL Error 6:
## /tmp/RtmpXsEdc9/simOutputs/landscape_year3.tif, band 1: SetColorTable() not
## supported for multi-sample TIFF files.

## Warning: [writeRaster] change datatype to INT1U to write the color-table
## could not write the color table

dev.off()

## png 
##   2

unlink(normalizePath(ftmp))

### WITHIN A MODULE:

# schedule a recurring save event
nextSave <- time(mySim) + params(mySim)$randomLandscapes$.saveInterval
sim <- scheduleEvent(mySim, nextSave, "randomLandscapes", "save")

Plotting

Plotting events can be scheduled similarly to save events, by passing module-specific parameters indicating when to schedule the first plot event and how often to rescheduling recurring plot events. By default, when a module is created with a newModule() call, plotting will be scheduled using .plotInitialTime. The default is NA, i.e., no plotting.

• .plotInitialTime: The time at which the first plot is scheduled.
• .plotInterval: The interval at which objects will be plotted. Used to schedule plot events.

# initialize a new simulation, setting the load and save parameters
mySim <- simInit(times = list(start = 0, end = 5),
                 params = list(
                   .globals = list(stackName = "landscape"),
                   randomLandscapes = list(.plotInitialTime = 0, .plotInterval = 1)
                 ),
                 modules = list("randomLandscapes"),
                 paths = list(modulePath = getSampleModules(tempdir()))
)

# retrieve the plotting params from the simulation object
params(mySim)$randomLandscapes$.plotInitialTime
params(mySim)$randomLandscapes$.plotInterval

### WITHIN A MODULE:

# schedule a recurring plot event
nextPlot <- time(mySim) + params(mySim)$randomLandscapes$.plotInterval
mySim <- scheduleEvent(mySim, nextPlot, "randomLandscapes", "save")

Included sample modules

Use getSampleModules(tempdir()) to get the path to a copy of the sampleModules directory. To view the contents of a module, use the openModules function. Note that you should not edit the sample modules directly, but save them as a copy in another location.

openModules(getSampleModules(tempdir()), "moduleName")

1. randomLandscapes: Generate RasterStack of random maps representative of a forest landscape (DEM, forestAge, habitatQuality, percentPine). Requires a global simulation parameter stackName be set.

2. fireSpread: Simulate fire ignition and spread on a landscape, where spread probability varies according to percent pine. Fire size statistics are collected immediately after each burn event. Requires a global simulation parameter stackName be set.

3. caribouMovement: Agent based model of caribou movement via correlated random walk. Requires a RasterStack object whose name is specified by globals(sim)$stackName, containing a RasterLayer named habitatQuality.

SpaDES module repository

Website: https://github.com/PredictiveEcology/SpaDES-modules

Modules from this (or another suitable GitHub repository) can be downloaded using:

downloadModule("moduleName")

Note: by default, modules and their data are saved to the directory specified by the spades.modulesPath. An alternate path can be provided to downloadModule directly via the path argument, or specified using options(spades.modulesPath = "path/to/my/modules").

Module design considerations

As noted above, modules should function as independently as possible so that they retain their modularity. While it may be useful for modules to exhibit indirect dependence on each other via shared data objects (such as maps), modules should not depend directly on each other via event scheduling.

Using the module template

Code for new modules can be developed quickly using the template generator function newModule(also available as an RStudio add-in) which accepts as arguments the name of the new module, a directory path in which to create the new module, and a logical indicating whether to open the newly created module code file for editing.

Module developers can set the devtools.desc.author option (e.g., in their .Rprofile to automatically supply a default module author). See package?devtools for more details.

# create a new module called "randomLandscape" in the "custom-modules" subdirectory
# and open the resulting file immediately for editing.
newModule(name = "randomLandscapes", path = "custom-modules", open = TRUE)

The newly created file can now be modified in the identified sections and customized to your module. However, it is very important that you do not edit portions of the file outside of the designated sections or your module may fail to work properly within a simulation.

Module groups

A ‘module group’ is simply a convenient way of specifying multiple modules. Module groups are specified by naming child modules in the defineModule block of the metadata. For example, the SpaDES_sampleModules module group lists the following modules as “children”:

• caribouMovement
• fireSpread
• randomLandscapes

Module groups make loading multiple modules easier: only the name of the module group needs to be specified in the simInit call, which will then initialize the simulation with the child modules.

library(DiagrammeR)
library(SpaDES.core)

outputDir <- file.path(tempdir(), "simOutputs")
times <- list(start = 0.0, end = 20.0)
parameters <- list(
  .globals = list(stackName = "landscape", burnStats = "nPixelsBurned"),
  .progress = list(NA),
  randomLandscapes = list(nx = 100L, ny = 100L, inRAM = TRUE),
  fireSpread = list(
    nFires = 10L, spreadprob = 0.225, its = 1e6, persistprob = 0,
    returnInterval = 10, startTime = 0,
    .plotInitialTime = 0, .plotInterval = 10
  ),
  caribouMovement = list(
    N = 100L, moveInterval = 1, torus = TRUE,
    .plotInitialTime = 1, .plotInterval = 1
  )
)
modules <- list("SpaDES_sampleModules")
objects <- list()
paths <- list(
  modulePath = getSampleModules(tempdir()),
  outputPath = outputDir
)

mySim <- simInit(times = times, params = parameters, modules = modules,
                 objects = objects, paths = paths)

modules(mySim) # note the child modules are initialized

Module groups can also be used to simplify the download of multiple modules:

downloadModule("SpaDES_sampleModules")

Debugging

Various debugging techniques are discussed at https://github.com/PredictiveEcology/SpaDES/wiki/Debugging.

Contributing modules

After publication you may wish to make your module more easily accessible for others to use (in fact we encourage it). We maintain a repository of SpaDES modules at the following site: https://github.com/PredictiveEcology/SpaDES-modules