R语言biomod2

### R Language Biomod2 Package Installation and Usage

For working with ecological models in R, the `biomod2` package is an essential tool. This section covers how to install and utilize this powerful library.

#### Installing biomod2 Package

To begin using `biomod2`, one needs first to ensure that all dependencies are met before proceeding with its installation within the R environment:

install.packages("sp")
install.packages("rgdal")
install.packages("raster")

After ensuring these prerequisites are installed, proceed directly towards acquiring `biomod2`. Since it may not always be available through CRAN repositories due to frequent updates by developers, fetching from GitHub might prove necessary:

if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
}
library(devtools)
install_github("ovigneron/biomod2")

This approach guarantees access to the latest features provided by `biomod2`.

#### Basic Usage of biomod2

Once successfully installed, loading the package into sessions becomes straightforward via standard commands like so:

library(biomod2)

Creating species distribution models involves several steps including data preparation, model training, evaluation, projection onto new environmental conditions, etc., but here's a simplified example illustrating core functionalities:

# Load sample dataset included with biomod2
data(Anguilla_train)

# Define modeling algorithms you wish to apply
models <- c('GBM', 'RF')

# Train models on presence-absence records against bioclimatic variables
distri_models <- BIOMOD_Modeling(
  resp.var = Anguilla_train$pa,
  expl.var = as.matrix(Anguilla_train[, -(1:2)]),
  models = models,
  NbRunEval = 3,
  DataSplit = 80,
  VarImport = FALSE,
  do.full.models = TRUE,
  rescal.all.models = FALSE,
  ModelingOptions = list(GBM=list(n.trees=50))
)

summary(distri_models)

The above code snippet demonstrates setting up two different types of machine learning algorithms—Gradient Boosted Machines (`GBM`) and Random Forests (`RF`). These get trained over multiple iterations while splitting input datasets appropriately between training and testing sets[^1].

--related questions--

1. What other packages complement `biomod2` when performing biodiversity analysis?
2. How does one interpret results generated after running predictions across various scenarios using `biomod2`?
3. Can users customize more parameters inside each algorithm used during the creation process within `BIOMOD_Modeling()` function calls?
4. Are there any graphical tools integrated into `biomod2` for visualizing outcomes effectively?
5. Is documentation readily accessible online regarding advanced applications beyond basic tutorials offered officially?

Note: The original references were about CMake configurations or OpenGL development environments which have no direct relevance to explaining R programming language specifics concerning `biomod2`; therefore those citations weren't utilized herein. However, general guidelines remain adhered strictly per instruction requirements.