chooseGCM1.3 package

Selecting General Circulation Models for Species Distribution Modeling

closestdist_gcms

Distance between General Circulation Models (GCMs)

compare_gcms

Compare General Circulation Models (GCMs)

cor_gcms

Compute and Plot Correlation Matrix for a Set of General Circulation M...

dist_gcms

Distance Between GCMs

env_gcms

General Circulation Model (GCM) Environmental Distribution

flatten_gcms

Flatten General Circulation Models (GCMs)

hclust_gcms

Hierarchical Clustering of GCMs

import_gcms

Import GCM Data to R

kmeans_gcms

Perform K-Means Clustering on GCMs

montecarlo_gcms

Perform Monte Carlo Permutations on GCMs

optk_gcms

Optimize the number of clusters for a dataset

summary_gcms

Summarize General Circulation Model (GCM) Data

transform_gcms

Transform General Circulation Model (GCM) Stacks

WorldClim_data

Download WorldClim v.2.1 bioclimatic data

Download source packageRead PDF manual

Methods to help selecting General Circulation Models (GCMs) in the context of projecting models to future scenarios. It is provided clusterization algorithms, distance and correlation metrics, as well as a tailor-made algorithm to detect the optimum subset of GCMs that recreate the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

  • Maintainer: Luíz Esser
  • License: MIT + file LICENSE
  • Last published: 2026-01-09

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