canaper VS biodivMapR

Compare canaper vs biodivMapR and see what are their differences.

canaper

Categorical Anaysis of Neo- and Paleo-endemism in R (by ropensci)

biodivMapR

biodivMapR: an R package for α- and β-diversity mapping using remotely-sensed images (by jbferet)
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canaper biodivMapR
1 2
7 35
- -
5.8 6.7
12 months ago 3 days ago
R R
GNU General Public License v3.0 or later GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

canaper

Posts with mentions or reviews of canaper. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-30.

biodivMapR

Posts with mentions or reviews of biodivMapR. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing canaper and biodivMapR you can also consider the following projects:

aorsf - Accelerated Oblique Random Survival Forests

awesome-spectral-indices - A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

melt - Multiple empirical likelihood tests

sentinel2-cloud-detector - Sentinel Hub Cloud Detector for Sentinel-2 images in Python

whiteboxR - WhiteboxTools R Frontend

MODIStsp - An "R" package for automatic download and preprocessing of MODIS Land Products Time Series

continuous-reforestation - Make continuous reforestation part of your daily workflow :deciduous_tree:

gn_mobile_occtax - Application mobile pour la saisie dans le module Occtax de GeoNature

eoreader - Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.