ehymet: 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.

Version: 0.1.0
Depends: R (≥ 4.1)
Imports: kernlab, stats, tf
Suggests: ggplot2, knitr, MASS, rmarkdown, testthat (≥ 3.0.0), tidyr
Published: 2024-06-14
DOI: 10.32614/CRAN.package.ehymet
Author: Belen Pulido ORCID iD [aut, cre], Jose Ignacio Diez [ctr]
Maintainer: Belen Pulido <bpulidob4 at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: ehymet results


Reference manual: ehymet.pdf
Vignettes: Introduction to ehymet
Clustering through the epigraph and hypograph indices


Package source: ehymet_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ehymet_0.1.0.tgz, r-oldrel (arm64): ehymet_0.1.0.tgz, r-release (x86_64): ehymet_0.1.0.tgz, r-oldrel (x86_64): ehymet_0.1.0.tgz


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