### Running a batch landscape simulation

Source: https://fmestre1.github.io/MetaLandSim/reference/manage_landscape_sim.html

Example showing the setup of simulation parameters, creation of the required data frame, and execution of the simulation.

```R
#Setup the parameters for each simulation:
PAR1_SPAN2 <- rep("ncsd",820)#parameter 1 for the span function 
PAR2_SPAN2 <- rep(seq(from=0,to=80,by=2), each=20)#parameter 2 for the span function 
PAR3_SPAN2 <- rep(seq(from=0,to=80,by=2),20)#parameter 3 for the span function 
PAR4_SPAN2 <- rep(2,820)#parameter 4 for the span function 
PAR5_SPAN2 <- rep(2,820)#parameter 5 for the span function 
NSEW_SPECIES2 <- rep("none",820)#where to start populating the landscape 
PARM_SPECIES2 <- rep(5,820)#parameter for the species function 
METHOD_SPECIES2 <- rep("percentage",820)#method for populating the landscape 
MAPSIZE2 <- rep(10000,820)#dimension of the landscape 
SPAN2 <- rep(100,820)#number of time steps of each simulation 
ITER2 <- rep(5,820)#number of iterations of each simulation 
NPATCH2 <- rep(800,820)#number of patches 
AREA_M2 <- rep(0.45,820)#mean area 
AREA_SD2 <- rep(0.2,820)#area sd 
MDST2 <- rep(0,820)#minimum distance between 
KERN <- rep("op1",820)#kernel
CONN <- rep("op1",820)#connectivity function 
COLNZ <- rep("op1",820)#colonization function 
EXT <- rep("op1",820)#extinction function 
BETA1 <- rep("NULL",820) 
B <- rep(1,820) 
C1 <- rep("NULL",820) 
C2 <- rep("NULL",820) 
Z <- rep("NULL",820) 
R2 <- rep("NULL",820) 
DISPERSAL2 <- rep(800,820)#mean dispersal ability of the species
SUCC <- rep("early",820)


#Build parameter data frame (keep the order of the parameters):
simulation <- data.frame(MDST2,NPATCH2,AREA_M2,AREA_SD2,
MAPSIZE2,SPAN2,ITER2,PAR1_SPAN2,PAR2_SPAN2,PAR3_SPAN2,PAR4_SPAN2,PAR5_SPAN2,
NSEW_SPECIES2,PARM_SPECIES2,METHOD_SPECIES2,KERN,CONN,COLNZ,EXT,BETA1,B,C1,C2,Z,R2,DISPERSAL2,SUCC)


#Delete vectors used for data frame creation:
rm('PAR1_SPAN2','PAR2_SPAN2','PAR3_SPAN2','PAR4_SPAN2','PAR5_SPAN2',
'NSEW_SPECIES2','PARM_SPECIES2','METHOD_SPECIES2','MAPSIZE2','SPAN2','ITER2',
'NPATCH2','AREA_M2','AREA_SD2','MDST2','KERN','CONN','COLNZ','EXT',
'BETA1','B','C1','C2','Z','R2','DISPERSAL2','SUCC')


if (FALSE) { # \dontrun{
data(param1)

ms2 <- manage_landscape_sim(par_df=simulation,parameters_spom=param1)
} # }
```

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

### Example of components.graph

Source: https://fmestre1.github.io/MetaLandSim/reference/components.graph.html

An example demonstrating how to use components.graph with the rland dataset.

```R
data(rland)

components.graph(rl=rland)
#> [1] 19
```

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

### Install MetaLandSim development version from GitHub

Source: https://fmestre1.github.io/MetaLandSim/index.html

Install the development version using devtools. Ensure devtools is installed first.

```R
# Install devtools if not already installed
install.packages("devtools")

# Install MetaLandSim from GitHub
devtools::install_github("FMestre1/MetaLandSim")
```

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

### Initialization list example for ifm.naive.MCMC

Source: https://fmestre1.github.io/MetaLandSim/reference/ifm.naive.MCMC.html

Example of a named list used to initialize the MCMC chain.

```R
init1=list(alpha=runif(1,1,30), b=runif(1,0,5),y=runif(1,0,20),  
  
  e=runif(1,0,1),x=runif(1,0,5)).
```

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

### simulate_graph usage example

Source: https://fmestre1.github.io/MetaLandSim/reference/simulate_graph.html

An example demonstrating how to initialize data and call simulate_graph with specific landscape and parameter settings.

```R
data(rland)
data(landscape_change)
data(param1)

sim1 <- simulate_graph(rl=rland, 
      rlist=landscape_change, 
      simulate.start=TRUE, 
      method="percentage", 
      parm=50, 
      nsew="none",
      succ = "none", 
      param_df=param1, 
      kern="op1", 
      conn="op1", 
      colnz="op1", 
      ext="op1", 
      beta1=NULL, 
      b=1, 
      c1=NULL, 
      c2=NULL, 
      z=NULL, 
      R=NULL
      )
```

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

### coda.create usage example

Source: https://fmestre1.github.io/MetaLandSim/reference/coda.create.html

An example demonstrating how to run an MCMC estimation and export the results using coda.create.

```R
if (FALSE) { # \dontrun{

# quick run; actual estimation requires more iterations:

data(simulatedifm)

myniter=100
nsite=nrow(z.sim)
nyear=ncol(z.sim)
nthin=1
nburnin=0
init1=list(alpha=runif(1,1,30), b=runif(1,0,5),y=runif(1,0,20),e=runif(1,0,1),x=runif(1,0,5))

inm1 <- ifm.naive.MCMC(niter=myniter,init=init1,z.data =
 z.sim,site.distance=sim.distance,site.area=sim.area,
  sd.prop.alpha=4,sd.prop.b=0.6,sd.prop.y=40,sd.prop.e=0.05,sd.prop.x=0.4,nthin=1,print.by=1000)

# write files in OpenBUGS format to working directory:
coda.create(inm1,"sim_inm1",par.list=list("e.chain","x.chain","alpha.chain",
"b.chain","y.chain"),niter=myniter,nthin=nthin)
} # }
```

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

### Example usage of manage_expansion_sim

Source: https://fmestre1.github.io/MetaLandSim/reference/manage_expansion_sim.html

An example showing how to call manage_expansion_sim with specific parameters for a range expansion simulation.

```R
if (FALSE) { # \dontrun{

data(param1)


sim_range <- manage_expansion_sim(mapsize=1000, dist_m=0, areaM, areaSD=0.001, 
                                  patch=300,percI=50, param=param1, b=1, 
                                  tsteps=100, iter=100,variable="area",var_min=0.01,
                                  var_max=0.6,by=0.1)
} # }
```

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

### Example usage of summary_landscape

Source: https://fmestre1.github.io/MetaLandSim/reference/summary_landscape.html

Example demonstrating how to load sample data and generate a summary for a landscape object.

```R
data(rland)

summary_landscape(object=rland)
#>                                             Value
#> landscape area (hectares)                 100.000
#> number of patches                          60.000
#> mean patch area (hectares)                  0.223
#> SD patch area                               0.091
#> mean distance amongst patches (meters)    492.910
#> minimum distance amongst patches (meters)  20.872

#                                            Value
#landscape area (hectares)                 100.000
#number of patches                          60.000
#mean patch area (hectares)                  0.061
#SD patch area                               0.041
#mean distance amongst patches (meters)    528.345
#minimum distance amongst patches (meters)  51.780
```

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

### Example: Calculating acceptance rates for a naive IFM model

Source: https://fmestre1.github.io/MetaLandSim/reference/accept.calculate.html

This example demonstrates initializing a chain, running the ifm.naive.MCMC function, and calculating the acceptance rates using accept.calculate.

```R
data(simulatedifm)

# Here, we run a chain with random initial values:
init1=list(alpha=runif(1,1,30), b=runif(1,0,5),y=runif(1,0,20),e=runif(1,0,1),x=runif(1,0,5))

inm1 <- ifm.naive.MCMC(niter=1000,init=init1,z.data =
 z.sim,site.distance=sim.distance,site.area=sim.area,
  sd.prop.alpha=4,sd.prop.b=0.6,sd.prop.y=40,sd.prop.e=0.05,sd.prop.x=0.4,nthin=1,print.by=100)
#> [1] 1
#> [1] 101
#> [1] 201
#> [1] 301
#> [1] 401
#> [1] 501
#> [1] 601
#> [1] 701
#> [1] 801
#> [1] 901
accept.calculate(inm1,model='naive')
#> $e.chain
#> [1] 0.493
#> 
#> $x.chain
#> [1] 0.341
#> 
#> $y.chain
#> [1] 0.547
#> 
#> $b.chain
#> [1] 0.211
#> 
#> $alpha.chain
#> [1] 0.216
#> 
#> $deviance.chain
#> [1] 0.912
#>
```

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

### Example of plotting expansion simulations

Source: https://fmestre1.github.io/MetaLandSim/reference/plot_expansion.html

An example showing how to load simulation data and visualize it using plot_expansion.

```R
data(rg_exp)

plot_expansion(exp = rg_exp)
#> starting httpd help server ...
#>  done
```

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

### addpoints example usage

Source: https://fmestre1.github.io/MetaLandSim/reference/addpoints.html

Example demonstrating how to add 10 patches to an existing landscape object and verify the change in the number of patches.

```R
data(rland)

#Checking the number of patches in the starting landscape:

rland$number.patches
#> [1] 60

#60

#Adding 10 patches to a landscape:

rl1 <- addpoints(rl=rland, nr=10)

#Checking the number of patches in the output landscape:

rl1$number.patches
#> [1] 70

#70
```

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

### Example usage of range_expansion

Source: https://fmestre1.github.io/MetaLandSim/reference/range_expansion.html

An example showing how to run the range_expansion function with sample data. Note that this should be run with more than 100 iterations.

```R
if (FALSE) { # \dontrun{
#Produce a model of range expansion:
#Note: this function should be run with >100 iterations (parameter "iter").

data(rland)
data(param2)

rg_exp1 <- range_expansion(rl=rland, percI=80, param=param2, b=1, tsteps=100, iter=100)

} # }
```

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

### Extracting a landscape example

Source: https://fmestre1.github.io/MetaLandSim/reference/extract.graph.html

Example showing how to extract the landscape at the 50th time step using rland and landscape_change data.

```R
data(rland)
data(landscape_change)

#Extracting the landscape of the 50th time step:

rl1 <- extract.graph(rl=rland, rlist=landscape_change, nr=50)
```

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

### Initialization list example for ifm.missing.MCMC

Source: https://fmestre1.github.io/MetaLandSim/reference/ifm.missing.MCMC.html

Example of a named list used to initialize the MCMC chain for the ifm.missing.MCMC function.

```R
init1=list(z.missing=runif(nmissing),mupsi1=runif(1),alpha=runif(1,1,30),   
b=runif(1,0,5),y=runif(1,0,20),  
  
e=runif(1,0,1),x=runif(1,0,5)).
```

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

### Examples of plot_graph usage

Source: https://fmestre1.github.io/MetaLandSim/reference/plot_graph.html

Examples showing how to plot a landscape without species occupancy and with species occupancy.

```R
data(rland)
data(occ.landscape)

#Without the species occupancy:
plot_graph(rl=rland, species=FALSE, links=FALSE)


#With the species occupancy:
plot_graph(rl=occ.landscape, species=TRUE, links=FALSE)
```

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

### import.shape example usage

Source: https://fmestre1.github.io/MetaLandSim/reference/import.shape.html

An example of how to call import.shape with the required parameters for filename, path, column names, and dispersal ability.

```R
if (FALSE) { # \dontrun{

rl1 <- import.shape(filename = "yourshapefile.shp"
      ,path = "C:/yourpath..."
      ,species.col= "column with species"
      ,ID.col="column with patch Id"
      ,area.col="Column with area"
      ,dispersal=800#Mean dispersal ability of the species 
      #(used to generate patch clusters, or components)
      )

} # }
```

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

### Reclassify clusters example

Source: https://fmestre1.github.io/MetaLandSim/reference/cluster.id.html

Example demonstrating how to remove patches from a landscape and reclassify the remaining components using cluster.id, followed by comparing component counts.

```R
data(rland)

#After removing 30 (50%) of the patches of a landscape:

rland2 <- removepoints(rl=rland, nr=35)

#A reclassification might be needed to identify components: 

rland2 <- cluster.id(rl=rland2)

#After removing 35 patches, there's a different number of components:

components.graph(rl=rland) 
#> [1] 19

#21

components.graph(rl=rland2) 
#> [1] 20

#16
```

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

### Example usage of ifm.missing.MCMC

Source: https://fmestre1.github.io/MetaLandSim/reference/ifm.missing.MCMC.html

A template for setting up the environment and parameters before running the MCMC estimation.

```R
if (FALSE) { # \dontrun{

data(simulatedifm)
library("coda")

niter=2000
nsite=100
nyear=10
nthin=1
nburnin=1000
## NOTE! The notation used here corresponds to MetaLandSim and differs from Risk et al 2011
## Here
## e (in MetaLandSim) = mu (in Risk et al 2011)
## x = chi
## y = gamma
## b = beta
## alpha = alpha
##
```

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

### Example usage of ifm.naive.MCMC

Source: https://fmestre1.github.io/MetaLandSim/reference/ifm.naive.MCMC.html

A template for running the MCMC estimation with simulated data.

```R
if (FALSE) { # \dontrun{
data(simulatedifm)

library("coda")

myniter=5000
nsite=nrow(z.sim)
nyear=ncol(z.sim)
nthin=1
nburnin=1000
## NOTE! The notation used here corresponds to MetaLandSim and differs from Risk et al 2011
## Here
## e (in MetaLandSim) = mu
## x = chi
## y = gamma
## b = beta
## alpha = alpha
##
```

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

### Summarizing metapopulation objects

Source: https://fmestre1.github.io/MetaLandSim/reference/summary_metapopulation.html

Examples of summarizing different metapopulation datasets, including the expected output format.

```R
data(occ.landscape)
data(occ.landscape2)

summary_metapopulation(object=occ.landscape)
#>                                             Value
#> landscape area (hectares)                 100.000
#> number of patches                          60.000
#> mean patch area (hectares)                  0.061
#> SD patch area                               0.041
#> mean distance amongst patches (meters)    528.345
#> minimum distance amongst patches (meters)  51.780
#> species occurrence - snapshot 1            50.000

#                                            Value
#landscape area (hectares)                 100.000
#number of patches                          60.000
#mean patch area (hectares)                  0.061
#SD patch area                               0.041
#mean distance amongst patches (meters)    528.345
#minimum distance amongst patches (meters)  51.780
#species occurrence - snapshot 1            50.000


summary_metapopulation(object=occ.landscape2)
#>                                             Value
#> landscape area (hectares)                 100.000
#> number of patches                          60.000
#> mean patch area (hectares)                  0.069
#> SD patch area                               0.039
#> mean distance amongst patches (meters)    521.717
#> minimum distance amongst patches (meters)  45.905
#> species occurrence - snapshot 1            50.000
#> species occurrence - snapshot 2            58.333
#> species occurrence - snapshot 3            61.667
#> species occurrence - snapshot 4            61.667
#> species occurrence - snapshot 5            58.333
#> species occurrence - snapshot 6            60.000
#> species occurrence - snapshot 7            70.000
#> species occurrence - snapshot 8            68.333
#> species occurrence - snapshot 9            68.333
#> species occurrence - snapshot 10           56.667

#                                            Value
#landscape area (hectares)                 100.000
#number of patches                          60.000
#mean patch area (hectares)                  0.069
#SD patch area                               0.039
#mean distance amongst patches (meters)    521.717
#minimum distance amongst patches (meters)  45.905
#species occurrence - snapshot 1            50.000
#species occurrence - snapshot 2            58.333
#species occurrence - snapshot 3            61.667
#species occurrence - snapshot 4            61.667
#species occurrence - snapshot 5            58.333
#species occurrence - snapshot 6            60.000
#species occurrence - snapshot 7            70.000
#species occurrence - snapshot 8            68.333
#species occurrence - snapshot 9            68.333
#species occurrence - snapshot 10           56.667
```

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

### Basic MetaLandSim workflow

Source: https://fmestre1.github.io/MetaLandSim/index.html

A basic example demonstrating how to load the package, generate a random landscape, simulate dynamics, plot the results, and compute connectivity metrics.

```R
# Load the package
library(MetaLandSim)

# Generate a random landscape with 100 patches
landscape <- rland.graph(npatch = 100, areaM = 50, distM = 1000)

# Simulate metapopulation dynamics over 20 time steps
simulation <- iterate.graph(rl = landscape, npatch = 100, nyears = 20)

# Plot the resulting landscape
plot_graph(landscape)

# Compute connectivity metrics
metrics <- metrics.graph(rl = landscape, metric = "NC")  # Number of components
```

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

### Install MetaLandSim from CRAN

Source: https://fmestre1.github.io/MetaLandSim/index.html

Use this command to install the stable version of the package from the CRAN repository.

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

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

### Example usage of plotL.graph

Source: https://fmestre1.github.io/MetaLandSim/reference/plotL.graph.html

Example showing how to plot the 50th landscape from a sequence of landscape changes.

```R
data(rland)
data(landscape_change)

plotL.graph(rl=rland, rlist=landscape_change, nr=50, species=FALSE, links=FALSE)
```

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

### Example of creating and displaying a parameter data frame

Source: https://fmestre1.github.io/MetaLandSim/reference/create.parameter.df.html

Demonstrates initializing a parameter data frame with specific values and displaying the resulting object.

```R
param2 <- create.parameter.df(alpha=0.5, x=0.1, y=5, e=0.1)

param2
#>       par_output
#> alpha        0.5
#> x            0.1
#> y            5.0
#> e            0.1

#      par_output
#alpha        0.5
#x            0.1
#y            5.0
#e            0.1
```

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

### edge.graph Example

Source: https://fmestre1.github.io/MetaLandSim/reference/edge.graph.html

Example showing how to generate an edge data frame using the rland dataset.

```R
data(rland)

edge_df <- edge.graph(rl=rland)
```

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

### Example of removing species occupancy

Source: https://fmestre1.github.io/MetaLandSim/reference/remove.species.html

Example demonstrating the conversion of a metapopulation object to a landscape object using remove.species.

```R
data(occ.landscape)

rl1 <- remove.species(sp=occ.landscape)
```

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

### Calculating and visualizing landscape statistics

Source: https://fmestre1.github.io/MetaLandSim/reference/list.stats.html

Example demonstrating how to simulate landscape occupation using simulate_graph and then evaluate the species occupancy statistics with list.stats.

```R
data(rland)
data(landscape_change)
data(param1)

#First, using simulate graph, simulate the occupation on a dynamic landscape 
#(output of span.graph):

sim1 <- simulate_graph(  rl=rland, rlist=landscape_change, simulate.start=TRUE, 
      method="percentage", parm=50, nsew="none", succ = "none",
      param_df=param1, kern="op1",  conn="op1", colnz="op1", 
      ext="op1", beta1=NULL, b=1, c1=NULL, c2=NULL, z=NULL, R=NULL)

#Then evaluate species occupancy through the changes suffered by the landscape:

occ <- list.stats(sim_list=sim1, stat="occupation", plotG=TRUE)


#Checking the percentage of occupation in the 40 first landscapes:

head(occ,40)
#>  [1] 50.000000 90.000000 86.666667 83.333333 66.666667 65.000000 65.000000
#>  [8] 60.000000 55.000000 48.333333 45.000000 33.333333 33.333333 25.000000
#> [15] 21.666667 18.333333 15.000000 10.000000  8.333333  5.000000  6.666667
#> [22]  6.666667  6.666667  3.333333  3.333333  3.333333  3.333333  3.333333
#> [29]  3.333333  1.666667  1.666667  0.000000  0.000000  0.000000  0.000000
#> [36]  0.000000  0.000000  0.000000  0.000000  0.000000

#Output:

#[1] 50.000000 65.000000 90.000000 96.666667 93.333333 91.666667
#[7] 91.666667 90.000000 93.333333 90.000000 85.000000 83.333333
#[13] 85.000000 88.333333 83.333333 86.666667 81.666667 68.333333
#[19] 70.000000 75.000000 80.000000 73.333333 63.333333 56.666667
#[25] 55.000000 51.666667 46.666667 41.666667 38.333333 21.666667
#[31] 13.333333 13.333333 10.000000  6.666667  5.000000  3.389831
#[37]  1.694915  1.694915  0.000000  0.000000
```

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

### Example usage of cluster.graph

Source: https://fmestre1.github.io/MetaLandSim/reference/cluster.graph.html

Demonstrates how to run cluster.graph using the rland dataset and shows the resulting data frame structure.

```R
data(rland)

cluster.graph(rl=rland)
#>    cluster number of nodes
#> 1        1               4
#> 2        2               1
#> 3        3               2
#> 4        4               9
#> 5        5               2
#> 6        6               3
#> 7        7              13
#> 8        8               2
#> 9        9               1
#> 10      10               2
#> 11      11               2
#> 12      12               3
#> 13      13               4
#> 14      14               1
#> 15      15               3
#> 16      16               4
#> 17      17               1
#> 18      18               2
#> 19      19               1

#Output:

#  cluster number of nodes
#1        1              11
#2        2               1
#3        3              13
#4        4               1
#5        5               1
#6        6              15
#7        7               2
#8        8               1
#9        9               3
#10      10               1
#11      11               1
#12      12               2
#13      13               4
#14      14               1
#15      15               1
#16      16               1
#17      17               1
```

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

### Create a Random Landscape Graph

Source: https://fmestre1.github.io/MetaLandSim/reference/rland.graph.html

Example demonstrating how to generate a landscape with 60 patches, a mean area of 0.05 hectares, and a dispersal ability of 120 meters.

```R
#Example to create a random landscape graph with 60 patches with a mean area 
#of 0.05 hectares. 
#The landscape mosaic is a square with 1000 meters side.
#The species mean dispersal ability is 120 meters (in order to connect the patches).
#A plot with the landscape graph is displayed graphically.

rl1 <- rland.graph(mapsize=1000, dist_m=80, areaM=0.05, areaSD=0.02, Npatch=60, 
      disp=120, plotG=TRUE)
```

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

### Example of removing patches from a landscape

Source: https://fmestre1.github.io/MetaLandSim/reference/removepoints.html

Demonstrates loading a landscape, checking the initial patch count, removing 10 patches, and verifying the new patch count.

```R
data(rland)

#Checking the number of patches in the starting landscape:

rland$number.patches
#> [1] 60

#60

#Removing 10 patches from the landscape:

rl1 <- removepoints(rl=rland, nr=10)

#Checking the number of patches in the output landscape:

rl1$number.patches
#> [1] 50

#50
```