sdmApp

species distribution models application

https://github.com/abson-dev/sdmapp

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 3 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (8.1%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

species distribution models application

Basic Info
  • Host: GitHub
  • Owner: Abson-dev
  • Language: CSS
  • Default Branch: master
  • Size: 10.1 MB
Statistics
  • Stars: 4
  • Watchers: 3
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Created over 5 years ago · Last pushed about 5 years ago
Metadata Files
Readme

README.md

sdmApp

Species Distribution Models Application

sdmApp: Statistical Details

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sdmApp is a R package containing a Shiny application that allows non-expert R users to easily model species distribution. It offers a reproducible work flow for species distribution modeling into a single and user friendly environment. sdmApp takes Raster data (in format supported by the Raster package) and species occurrence data (several format supported) as input argument. This package provides an interactive graphical user interface (GUI). This document will give an overview of the main functionalities of the graphical user interface. The main features of the GUI is:

  • Uploading data (raster and species occurrence files)
  • View correlation between raster
  • Use CENFA to select species predictors
  • Apply a spatial blocking for cross-validation based on the blockCV package
  • Apply species distribution models with or without a spatial blocking strategy

  • Export results

  • Keep reproduce (R code) by being able do download the underlying code from sdmApp.

The GUI is build around 5 main windows, which can be selected from the navigation bar at the top of the screen. Initially, some of these windows will be empty and their content changes once data (both raster and species occurrence files) have been uploaded.

Set working environment

sdmApp uses Maxent model which used the Java implementation. Therefore you need :

  • the Java JDK software is installed;
  • the package rJava is installed;
  • the file maxent.jar is copied in the correct folder.

Installation

To install the package from github use:

r remotes::install_github("Abson-dev/sdmApp", dependencies = TRUE) library(sdmApp) sdmApp() To install the package from CRAN use:

r install.package("sdmApp", dependencies = TRUE) library(sdmApp) sdmApp()

Citation

r citation("sdmApp")

To cite package sdmApp in publications use:

HEMA A, NDAO B, LEROUX L, DIOUF A (2021). sdmApp: A User-Friendly Application for Species Distribution Modeling. R package version 0.0.2, https://CRAN.R-project.org/package=sdmApp.

A BibTeX entry for LaTeX users is

@Manual{, title = {sdmApp: A User-Friendly Application for Species Distribution Modeling}, author = {Aboubacar HEMA and Babacar NDAO and Louise LEROUX and Abdoul Aziz DIOUF}, year = {2021}, note = {R package version 0.0.2}, url = {https://CRAN.R-project.org/package=sdmApp}, }

License

The sdmApp sticker was made through R art kindly shared by this link and released under a CC-BY-SA 4.0 license.

Guidelines for contributing

I welcome contributions and suggestions for improving this package. Please do not hesitate to submit any issues you may encounter.

Owner

  • Name: Aboubacar HEMA
  • Login: Abson-dev
  • Kind: user
  • Location: Dakar
  • Company: Statistician and Computer Scientist

I am Aboubacar HEMA, Statistician and Computer Scientist. I am highly proficient in R and Python (actually, eats and breaths R & Python).

GitHub Events

Total
Last Year

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 710
  • Total Committers: 1
  • Avg Commits per committer: 710.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Aboubacar HEMA 5****v 710

Issues and Pull Requests

Last synced: over 2 years ago

All Time
  • Total issues: 4
  • Total pull requests: 0
  • Average time to close issues: 16 days
  • Average time to close pull requests: N/A
  • Total issue authors: 4
  • Total pull request authors: 0
  • Average comments per issue: 0.75
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • Abson-dev (1)
  • rsbivand (1)
  • thomasp85 (1)
  • gok-wolf (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 252 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: sdmApp

A User-Friendly Application for Species Distribution Modeling

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 252 Last month
Rankings
Forks count: 21.9%
Stargazers count: 24.2%
Dependent packages count: 29.8%
Average: 32.8%
Dependent repos count: 35.5%
Downloads: 52.5%
Maintainers (1)
Last synced: over 2 years ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • raster >= 2.6.7 imports
  • shiny >= 0.12.2 imports
  • sp >= 1.2.0 imports
  • DT * suggests
  • SSDM >= 0.2.8 suggests
  • automap >= 1.0 suggests
  • blockCV >= 2.1.1 suggests
  • covr * suggests
  • cowplot >= 1.1.1 suggests
  • data.table * suggests
  • dismo >= 1.0.12 suggests
  • dplyr >= 1.0.3 suggests
  • future.apply * suggests
  • ggcorrplot >= 0.1.3 suggests
  • ggplot2 >= 3.1.1 suggests
  • grDevices * suggests
  • haven >= 2.3.1 suggests
  • kernlab >= 0.9 suggests
  • knitr * suggests
  • rJava >= 0.9 suggests
  • randomForest >= 4.6.10 suggests
  • readxl >= 1.3.1 suggests
  • rgdal >= 1.5 suggests
  • rgeos >= 0.3 suggests
  • rhandsontable >= 0.3.7 suggests
  • rmarkdown * suggests
  • sf * suggests
  • shinyBS >= 0.61 suggests
  • shinyFiles >= 0.7.0 suggests
  • stats * suggests
  • testthat * suggests
  • tidyr >= 1.1.2 suggests
  • utils * suggests