Package: SDMPlay
Type: Package
Title: Species Distribution Modelling Playground
Version: 2.0
Date: 2021-09-14
Authors@R: c(
    person("Guillaumot", "Charlene", email ="charleneguillaumot21@gmail.com",role=c("aut","cre")),
    person("Martin","Alexis",email = "alexis.martin@mnhn.fr",role = "aut"),
    person("Eleaume","Marc",email = "marc.eleaume@mnhn.fr",role = "aut"),
    person("Danis","Bruno",email = "bdanis@ulb.ac.be",role = "aut"),
    person("Saucede","Thomas",email = "thomas.saucede@u-bourgogne.fr",role = "aut"))
Maintainer: Guillaumot Charlene <charleneguillaumot21@gmail.com>
Description: Species distribution modelling (SDM) has been developed for several years to address conservation issues, assess the direct impact of human activities on ecosystems and predict the potential distribution shifts of invasive species (see Elith et al. 2006, Pearson 2007, Elith and Leathwick 2009). SDM relates species occurrences with environmental information and can predict species distribution on their entire occupied space. This approach has been increasingly applied to Southern Ocean case studies, but requires corrections in such a context, due to the broad scale area, the limited number of presence records available and the spatial and temporal aggregations of these datasets. SDMPlay is a pedagogic package that will allow you to compute SDMs, to understand the overall method, and to produce model outputs. The package, along with its associated vignettes, highlights the different steps of model calibration and describes how to choose the best methods to generate accurate and relevant outputs. SDMPlay proposes codes to apply a popular machine learning approach, BRT (Boosted Regression Trees) and introduces MaxEnt (Maximum Entropy). It contains occurrences of marine species and environmental descriptors datasets as examples associated to several vignette tutorials available at <https://github.com/charleneguillaumot/THESIS/tree/master/SDMPLAY_R_PACKAGE>.
License: GPL-3
LazyData: TRUE
Depends: R (>= 3.5.0)
Encoding: UTF-8
Imports: raster, dismo, stats, base
Suggests: maptools, testthat, grDevices, knitr, rmarkdown, markdown,
        ncdf4, rgdal, graphics, sp, rJava, gbm
RoxygenNote: 7.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-09-14 18:36:28 UTC; charlene
Author: Guillaumot Charlene [aut, cre],
  Martin Alexis [aut],
  Eleaume Marc [aut],
  Danis Bruno [aut],
  Saucede Thomas [aut]
Repository: CRAN
Date/Publication: 2021-09-15 06:40:05 UTC
Built: R 4.0.5; ; 2022-04-21 08:33:56 UTC; windows
