Methodologies for Functional Data Based on the Epigraph and Hypograph Indices

Implements methods for functional data analysis based on the epigraph and hypograph indices. These methods transform functional datasets, whether in one or multiple dimensions, into multivariate datasets. The transformation involves applying the epigraph, hypograph, and their modified versions to both the original curves and their first and second derivatives. The calculation of these indices is tailored to the dimensionality of the functional dataset, with special considerations for dependencies between dimensions in multidimensional cases. This approach extends traditional multivariate data analysis techniques to the functional data setting. A key application of this package is the EHyClus method, which enhances clustering analysis for functional data across one or multiple dimensions using the epigraph and hypograph indices. See Pulido et al. (2023) and Pulido et al. (2024) .

ehymet: Epigraph-Hypograph based methodologies for functional data

The ehymet package define the epigraph, the hypograph and their modified versions for functional datasets in one and multiple dimensions. These indices allow to transform a functional dataset into a multivariate one, where usual clustering techniques can be applied. This package implements EHyClus method for clustering functional data in one or multiple dimension.

Related Papers:

• Belén Pulido, Alba M. Franco-Pereira, Rosa E. Lillo (2023). “A fast epigraph and hypograph-based approach for clustering functional data.” Statistics and Computing, 33, 36. doi: 10.1007/s11222-023-10213-7

• Belén Pulido, Alba M. Franco-Pereira, Rosa E. Lillo (2024). “Clustering multivariate functional data using the epigraph and hypograph indices: a case study on Madrid air quality.” doi: 10.48550/arXiv.2307.16720

Installation

You can install the development version of ehymet from github using the remotes package:

# install.packages("remotes")
remotes::install_github("bpulidob/ehymet")