The goal of mvabund is to provide tools for a model-based approach to the analysis of multivariate abundance data in ecology (Yi Wang et al. 2011), in particular, testing hypothesis about the community-environment association. Abundance measures include counts, presence/absence data, ordinal or biomass data.
This package includes functions for visualising data, fitting predictive models, checking model assumptions, as well as testing hypotheses about the community–environment association.
mvabund is available on CRAN and can be
installed directly in R:
install.packages("mvabund")
library(mvabund)Alternatively, you can install the development
version of mvabund from GitHub with:
# install.packages("remotes")
remotes::install_github("eco-stats/mvabund")
library(mvabund)We highly recommend you taking a good read of our vignette over at our website before launching into the mvabund.
Alternatively, you can access the vignettes in R by:
remotes::install_github("eco-stats/mvabund", build_vignettes = TRUE)
vignette("mvabund")mvabund your
supportcitation("mvabund")
#> To cite package 'mvabund' in publications use:
#>
#> Wang Y, Naumann U, Eddelbuettel D, Wilshire J, Warton D (2022).
#> _mvabund: Statistical Methods for Analysing Multivariate Abundance
#> Data_. R package version 4.2.1,
#> <https://CRAN.R-project.org/package=mvabund>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {mvabund: Statistical Methods for Analysing Multivariate Abundance Data},
#> author = {Yi Wang and Ulrike Naumann and Dirk Eddelbuettel and John Wilshire and David Warton},
#> year = {2022},
#> note = {R package version 4.2.1},
#> url = {https://CRAN.R-project.org/package=mvabund},
#> }Thanks for finding the bug! We would appreciate it if you can pop over to our Issues page and describe how to reproduce the bug!
mvabund for comparing species
composition across different habitatsmvabundCheck out the list of studies that uses mvabund in their
analyses here