README.md

In emf-creaf/vegclust: Fuzzy Clustering of Vegetation Data

Fuzzy clustering of vegetation data

Introduction

Package vegclust is a package designed to assist analyses of vegetation structure and composition. It is intended to be useful for community ecologists and forest engineers, but the clustering functions can be used in other fields. The package provides functions to:

• Perform fuzzy clustering of vegetation data (De Cáceres et al. 2010).
• Assess ecological community ressemblance on the basis of structure and composition (De Cáceres et al. 2013).

Package installation

Package vegclust can be found at CRAN package repository. In addition, the latest stable vegclust R package can be installed from GitHub as follows:

devtools::install_github("emf-creaf/vegclust")

Additionally, users can have help to run package functions directly as package vignettes, by forcing their inclusion in installation:

devtools::install_github("emf-creaf/vegclust", 
                         build_opts = c("--no-resave-data", "--no-manual"),
                         build_vignettes = TRUE)

Note about ‘Community Trajectory Analysis’

Until ver. 2.0, package vegclust included functions to conduct Community Trajectory Analysis (CTA). Since ver. 2.0 these functions have been moved to an independent package ecotraj available at https://github.com/emf-creaf/ecotraj/ and also in CRAN.

Maintenance

Although not in active development, the R package is maintained by the Ecosystem Modelling Facility unit at CREAF (in Spain).

References

• De Cáceres, M., Font, X., & Oliva, F. 2010. The management of vegetation classifications with fuzzy clustering. Journal of Vegetation Science 21: 1138–1151 (https://doi.org/10.1111/j.1654-1103.2010.01211.x).

• De Cáceres, M., Legendre, P., & He, F. 2013. Dissimilarity measurements and the size structure of ecological communities (D. Faith, Ed.). Methods in Ecology and Evolution 4: 1167–1177 (https://doi.org/10.1111/2041-210X.12116).

emf-creaf/vegclust documentation built on July 27, 2024, 7:32 a.m.