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caretSDM

Luíz Fernando Esser

caretSDM

caretSDM is a under development R package that uses the powerful caret package as the main engine to obtain Species Distribution Models. As caret is a packaged turned to build machine learning models, caretSDM has a strong focus on this approach.

Installation

You can install the development version of caretSDM from GitHub with:

install.packages("devtools")
devtools::install_github("luizesser/caretSDM")

The package is also available on CRAN. Users are able to install it using the following code:

install.packages("caretSDM")

You need help?

caretSDM is vastly documented and has included some objects that can guide your data management. If some of your data or code seem to be wrong, try to take a look at those objects or the articles in the website:

Objects

• bioc Bioclimatic variables for current scenario in stars class.

• rivs Hydrological variables for current scenario in sf class.

• occ Araucaria angustifolia occurrence data as a dataframe.

• salm Salminus brasiliensis occurrence data as a dataframe.

• parana Shapefile to use in sdm_area in Simple Feature class.

• scen Bioclimatic variables for future scenarios in stars class.

• algorithms Dataframe with characteristics from every algorithm available in caretSDM.

Articles

• caretSDM Workflow for Species Distribution Modeling is the main vignette for terrestrial species modeling, where we model the tree species Araucaria angustifolia.

• Modeling Species Distributions in Continental Water Bodies is the main vignette for continental aquatic species modeling, where we model the fish species Salminus brasiliensis.

• Concatenate functions in caretSDM shows how to build compact scripts, which is very useful to run your first tests.

• Adding New Algorithms to caretSDM Do not found your ideal algorithm already implemented? Here we show how to implement any custom algorithm in our package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.