AICcmodavg 2.1-1

Model Selection and Multimodel Inference Based on (Q)AIC(c)

Released Jun 19, 2017 by Marc J. Mazerolle

This package is available for Renjin and there are no known compatibility issues.


Matrix 1.2-13 survival 2.41-3 nlme 3.1-131.1 unmarked 0.12-2 xtable 1.8-2 VGAM 1.0-5 MASS 7.3-49 lattice 0.20-35

Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs' and 'rjags' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.



This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

    <name>bedatadriven public repo</name>

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Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:


Test Results

This package was last tested against Renjin 0.9.2622 on Apr 8, 2018.