AIDA - Analysis of Interval 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.