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.

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.

Tools for the analysis of interval-valued data, including construction, 
    visualization, and statistical modeling. The package provides the 'intData' class for 
    representing interval-valued data, along with functions to aggregate microdata and 
    to estimate parameters of latent distributions.
    Barycenter and covariance matrix estimation is implemented based on the Mallows 
    distance (Oliveira et al. (2025) <doi:10.48550/arXiv.2407.05105>).
    Robust estimation of the symbolic covariance matrix is implemented via the Interval 
    Minimum Covariance Determinant (IMCD) estimator, enabling outlier detection 
    based on the robust squared Interval-Mahalanobis distance, as proposed by 
    Loureiro et al. (2026b) <doi:10.48550/arXiv.2604.26769>.
    Explainable outlier detection is supported through Shapley value based decomposition 
    of the squared robust Interval-Mahalanobis distance, allowing assessment of variable 
    contributions to outlyingness (Loureiro et al. (2026a) <doi:10.48550/arXiv.2606.26307>). 
    Shapley interaction indices are also implemented, along with visualization tools to 
    support interpretation of the results.