Learn R Programming

⚠️There's a newer version (1.2-55) of this package.Take me there.

sdm (version 1.2-46)

Species Distribution Modelling

Description

An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) .

Copy Link

Version

Install

install.packages('sdm')

Monthly Downloads

1,206

Version

1.2-46

License

GPL (>= 3)

Maintainer

Babak Naimi

Last Published

July 17th, 2024

Functions in sdm (1.2-46)

gui

Graphical User Interface
nicheSimilarity

Niche Similarity
pa

Converting probability of occurrence to Presence-absence
get models' outputs

Get information/modelIDs relevant to fitted models in a sdmModels object
read.sdm

read/write sdm* object from/to a file
rcurve

Generate and plot response curves
getVarImp

variable importance
pca

Principle Component Analysis
subset

Subset models in a sdmModels object
predict

sdm model prediction
sdmdata-class

An S4 class representing sdm dataset
Extract by index

Indexing to extract records of a sdmdata object
installAll

install all packages that may be required by the package
as.data.frame

Get a data.frame with record id values (rID)
Arith-methods

Combine (merge) two sdmModels into a single object
sdmCorrelativeMethod-class

sdmCorrelativeMethod class
names

Names of species
sdmModels-classes

sdmModels classes
sdmData

creating sdm Data object
niche

Generate and plot Ecological Niche
sdmSetting

creating sdmSetting object
roc

plot ROC curves
sdm

Fit and evaluate species distribution models
background

Generate background (pseudo-absence) records
sdmAdapt

Adapting sdm* objects in the new version
add

add a new method to the package
calibration

Calibration
boxplot

boxplot
coords

get or set spatial coordinates of species data
featuresFrame-class

featureFrame class
evaluates

evaluate for accuracy
density

density
ensemble

Ensemble Forecasting of SDMs