AIDA: Analysis of Interval DAta

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Overview

AIDA provides tools for the analysis of interval-valued data, including construction, visualization, robust estimation, and outlier detection. The R package is built around the intData class and is designed to support methodological research and applied workflows involving interval-valued data.

AIDA currently includes functionality for:

Installation

You can install the development version from GitHub:

# install.packages("pak")
pak::pak("catarinaploureiro/AIDA")

Minimal Example

library(AIDA)
#> 
#> Attaching package: 'AIDA'
#> The following object is masked from 'package:base':
#> 
#>     rbind

# Create an intData object from the iris dataset, using the Species column as 
# grouping variable. We also specify the latent distribution as "General" to 
# estimate the parameters based on the microdata.
data(iris)
iris_int <- micro2intData(iris[,1:4], iris$Species, LatentCase = "General")

# Check the parameters of the latent distribution
iris_int@LatentParam
#> [[1]]
#>           [,1]      [,2]      [,3]      [,4]
#> [1,] 0.1835903 0.1554868 0.1660030 0.1989812
#> [2,] 0.1554868 0.1381013 0.1463002 0.1685136
#> [3,] 0.1660030 0.1463002 0.1627440 0.1829350
#> [4,] 0.1989812 0.1685136 0.1829350 0.2704900
#> 
#> [[2]]
#>            [,1]       [,2]       [,3]       [,4]
#> [1,] 0.01851852 0.00000000 0.00000000  0.0000000
#> [2,] 0.00000000 0.04199507 0.00000000  0.0000000
#> [3,] 0.00000000 0.00000000 0.02913753  0.0000000
#> [4,] 0.00000000 0.00000000 0.00000000 -0.1802821

# Compute the classical sample barycenter, covariance and correlation matrices
iris_mean <- int_mean(iris_int)
iris_cov <- int_cov(iris_int)
iris_corr <- int_cor(iris_int)

# Pairs plot, the lower triangular shows scatter plots of the four variables, 
# while the upper triangular shows the interval correlation matrix.
plot_pairs_int(iris_int, corr = iris_corr, labels = colnames(iris_int))

Vignettes

For a full introduction about the intData class (Oliveira, Pinheiro, and Oliveira (2025)), see:

vignette("intData_examples", package = "AIDA")

For examples on the IMCD estimator and outlier detection based on the robust squared Interval-Mahalanobis distance (Loureiro et al. (2026b)), see:

vignette("IMCD_examples", package = "AIDA")

For examples on explainable outlier detection using Shapley values (Loureiro et al. (2026a)), see:

vignette("Shapley_examples", package = "AIDA")

References

Loureiro, Catarina P., M. Rosário Oliveira, Paula Brito, and Lina Oliveira. 2026a. “Explainable Outlier Detection for Interval-valued Data.” https://arxiv.org/abs/2606.26307.
———. 2026b. “Minimum Covariance Determinant Estimator and Outlier Detection for Interval-valued Data.” https://arxiv.org/abs/2604.26769.
Oliveira, M. Rosário, Diogo Pinheiro, and Lina Oliveira. 2025. “Location and association measures for interval data based on Mallows distance.” https://arxiv.org/abs/2407.05105.