Package: mvabund 4.2.1

David Warton

mvabund: Statistical Methods for Analysing Multivariate Abundance Data

A set of tools for displaying, modeling and analysing multivariate abundance data in community ecology. See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library (<http://www.gnu.org/software/gsl/>) and 'Rcpp' (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.

Authors:Yi Wang [aut], Ulrike Naumann [aut], Dirk Eddelbuettel [aut], John Wilshire [aut], David Warton [aut, cre], Julian Byrnes [ctb], Ralph dos Santos Silva [ctb, cph], Jenni Niku [ctb], Ian Renner [ctb], Stephen Wright [ctb]

mvabund_4.2.1.tar.gz
mvabund_4.2.1.tar.gz(r-4.5-noble)mvabund_4.2.1.tar.gz(r-4.4-noble)
mvabund_4.2.1.tgz(r-4.4-emscripten)mvabund_4.2.1.tgz(r-4.3-emscripten)
mvabund.pdf |mvabund.html✨
mvabund/json (API)

# Install 'mvabund' in R:
install.packages('mvabund', repos = 'https://cloud.r-project.org')
Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda-Forge:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

gslcpp

5.90 score 5 packages 728 scripts 1.5k downloads 112 mentions 53 exports 5 dependencies

Last updated 3 years agofrom:fc1441368c. Checks:1 OK, 1 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 19 2025
R-4.5-linux-x86_64NOTEFeb 19 2025

Exports:anova.manyanyanova.manyglmanova.manylmanova.traitglmas.mvabundbest.r.sqboxplot.mvabundboxplot.mvformulacoefplotcoefplot.manyglmcv.glm1pathdeviance.manylmextend.x.formulaformulaUnimvaglm1glm1pathis.mvabundis.mvformulamanyanymanyglmmanylmmeanvar.plotmvabundmvformulaplot.glm1pathplot.manyanyplot.manyglmplot.manylmplot.mvabundplot.mvformulaplotFormulafeatureplotMvaFactorpredict.manyglmpredict.manylmpredict.traitglmprint.anova.manyanyprint.anova.manyglmprint.anova.manylmprint.manyanyprint.manyglmprint.mvformulaprint.summary.manyglmprint.summary.manylmRcppVersionresiduals.glm1pathresiduals.manyanyresiduals.manyglmridgeParamEstshiftpointssummary.manyglmsummary.manylmtraitglmunabund

Dependencies:MASSRcppRcppGSLstatmodtweedie

Citation

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.

Corresponding BibTeX entry: 

  @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},
  }

Readme and manuals

mvabund: Statistical Methods for Analysing Multivariate Abundance Data

Authors

Yi Wang, Ulrike Naumann, Stephen Wright, Dirk Eddelbuettel and David Warton

License

LGPL (>= 2.1)

Installation

mvabund is available on CRAN.

The development version, with the latest bells and whistles, can be installed from GitHub using the devtools package:

devtools::install_github('aliceyiwang/mvabund', ref='main')
library(mvabund)

Help Manual

Help pageTopics
Statistical methods for analysing multivariate abundance datamvabund-package
Analysis of Deviance for Many Univariate Models Fitted to Multivariate Abundance Dataanova.manyany print.anova.manyany
Analysis of Deviance for Multivariate Generalized Linear Model Fits for Abundance Dataanova.manyglm print.anova.manyglm
ANOVA for Linear Model Fits for Multivariate Abundance Dataanova.manylm print.anova.manylm
Testing for a environment-by-trait (fourth corner) interaction by analysis of devianceanova.traitglm
Ant data, with species traitsantTraits
Use R^2 to find the variables that best explain a multivariate response.best.r.sq
Boxplots for multivariate abundance Databoxplot.mvabund boxplot.mvformula
Plots the coefficients of the covariates of a manyglm object with confidence intervals.coefplot coefplot.manyglm
Fits a path of Generalised Linear Models with LASSO (or L1) penalties, and finds the best model by corss-validation.cv.glm1path
Model Deviancedeviance.manylm
Extend a Formula to all of it's Termsextend.x.formula
Create a List of Univariate FormulasformulaUnimva
Fits a Generalised Linear Models with a LASSO (or L1) penalty, given a value of the penalty parameter.glm1
Fits a path of Generalised Linear Models with LASSO (or L1) penalties, and finds the model that minimises BIC.glm1path
Calculate the Log LikelihoodlogLik.manylm
Fitting Many Univariate Models to Multivariate Abundance Datamanyany print.manyany
Fitting Generalized Linear Models for Multivariate Abundance Datamanyglm
Fitting Linear Models for Multivariate Abundance Datamanylm
workhose functions for fitting multivariate linear modelsmanylm.fit manylm.wfit
Construct Mean-Variance plots for Multivariate Abundance Datameanvar.plot meanvar.plot,data.frame-method meanvar.plot,formula-method meanvar.plot,matrix-method meanvar.plot,mvabund-method meanvar.plot,mvformula-method meanvar.plot.mvabund meanvar.plot.mvformula
Multivariate Abundance Data Objectsas.mvabund is.mvabund mvabund mvabund-class
Model Formulae for Multivariate Abundance Dataas.mvformula is.mvformula mvformula mvformula-class
Plot Diagnostics for a manyany or glm1path Objectplot.glm1path plot.manyany
Plot Diagnostics for a manylm or a manyglm Objectplot.manyglm plot.manylm
Plot Multivariate Abundance Data and Formulaeplot.mvabund plot.mvformula
Draw a Mvabund Object split into groups.plotMvaFactor
Predict Method for MANYGLM Fitspredict.manyglm
Model Predictions for Multivariate Linear Modelspredict.manylm
Predictions from fourth corner model fitspredict.traitglm
Residuals for MANYGLM, MANYANY, GLM1PATH Fitsresiduals.glm1path residuals.manyany residuals.manyglm
Estimation of the ridge parameterridgeParamEst
Calculate a shift for plotting overlapping pointsshiftpoints
Solberg Datasolberg
Spider dataspider
Summarizing Multivariate Generalized Linear Model Fits for Abundance Dataprint.summary.manyglm summary.manyglm
Summarizing Linear Model Fits for Multivariate Abundance Dataprint.summary.manylm summary.manylm
Tasmania DatasetTasmania
Tikus Island Datasettikus
Fits a fourth corner model for abundance as a function of environmental variables and species traits.traitglm
Remove the mvabund Class Attributeunabund