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', 'rjags', and 'jagsUI' 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.

Provides tools for model selection and model averaging of PerMANOVA 
    models using Akaike Information Criterion corrected for small sample sizes 
    (AICc) and Information Theoretic criteria principles. The package is built 
    around the PERMANOVA analysis from the 'vegan' package and provides a 
    streamlined workflow for generating and comparing models, obtaining model 
    weights, and summarizing results using model averaging approaches.  The 
    methods implemented in this package are based on the practical information-
    theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) 
    (<doi:10.1007/b97636>).

Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.

Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.

A supportive collection of functions for pooled analysis of aggregate data. The current version supports users to test assumptions before relevant analysis of bias from study size and sequential analysis such as mentioned by Wetterslev, J., Jakobsen, J. C., & Gluud, C. (2017) <doi:10.1186/s12874-017-0315-7>.