Package: boral 2.0.2

Francis K.C. Hui

boral: Bayesian Ordination and Regression AnaLysis

Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.

Authors:Francis K.C. Hui [aut, cre], Wade Blanchard [aut]

boral_2.0.2.tar.gz
boral_2.0.2.tar.gz(r-4.5-noble)boral_2.0.2.tar.gz(r-4.4-noble)
boral_2.0.2.tgz(r-4.4-emscripten)boral_2.0.2.tgz(r-4.3-emscripten)
boral.pdf |boral.html✨
boral/json (API)

# Install 'boral' in R:
install.packages('boral', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

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

3.41 score 2 stars 72 scripts 653 downloads 9 mentions 24 exports 22 dependencies

Last updated 8 months agofrom:4522096fb4. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linuxOKNov 20 2024

Exports:boralboral.defaultcalc.condlogLikcalc.logLik.lv0calc.marglogLikcalc.varpartcoefsplotcreate.lifeds.residualsfitted.boralget.enviro.corget.hpdintervalsget.mcmcsamplesget.measuresget.more.measuresget.residual.corlvsplotmake.jagsboralmodelmake.jagsboralnullmodelplot.boralpredict.boralranefsplotsummary.boraltidyboral

Dependencies:abindbootclicodacorpcorfishModgluelatticelifecyclemagrittrMASSmvtnormplyrR2jagsR2WinBUGSRcppreshape2rjagsrlangstringistringrvctrs

Readme and manuals

Help Manual

Help pageTopics
Bayesian Ordination and Regression AnaLysis (boral)boral-package
Distributions available in boralabout.distributions
Correlation structure for latent variablesabout.lvs
Including response-specific random intercepts in boralabout.ranefs
Stochastic search variable selection (SSVS) in boralabout.ssvs
Including species traits in boralabout.traits
Fitting boral (Bayesian Ordination and Regression AnaLysis) modelsboral boral.default print.boral
Conditional log-likelihood for a fitted modelcalc.condlogLik
Log-likelihood for a model fitted with no latent variablescalc.logLik.lv0
Marginal log-likelihood for a fitted modelcalc.marglogLik
Variance partitioning for a latent variable modelcalc.varpart
Caterpillar plots of the regression coefficients from a fitted modelcoefsplot
\Sexpr[results=rd, stage=render]{lifecycle::badge("stable")} Simulate a Multivariate response matrixcreate.life simulate.boral
Dunn-Smyth Residuals for a fitted modelds.residuals
Extract Model Fitted Values for an boral objectfitted.boral
Extract Deviance Information Criterion for a fitted modelget.dic
Extract covariances and correlations due to shared environmental responsesget.enviro.cor
Highest posterior density intervals for a fitted modelget.hpdintervals
Extract MCMC samples from modelsget.mcmcsamples
Information Criteria for modelsget.measures
Additional Information Criteria for modelsget.more.measures
Extract residual correlations and precisions from modelsget.residual.cor
Plot the latent variables from a fitted modellvsplot
Write a text file containing a model for use into JAGSmake.jagsboralmodel
Write a text file containing a model for use into JAGSmake.jagsboralnullmodel
Plots of a fitted boral objectplot.boral
Predict using a modelpredict.boral
Caterpillar plots of response-specific random effects from a fitted modelranefsplot
Summary of fitted boral objectprint.summary.boral summary.boral
Reformats output from a boral fittidyboral