R: Methodologies for Functional Data Based on the Epigraph and Hypograph Indices

# Methodologies for Functional Data Based on the Epigraph and Hypograph Indices

## Documentation for package ‘ehymet’ version 0.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

clustering_validation | Create a table containing four validation metrics for clustering: Purity, F-measure and Rand Index (RI) and Adjusted Rand Index (ARI). This function considers pairs of points
clustInd_hierarch | Perform hierarchical clustering for a different combinations of indices, method and distance
clustInd_kkmeans | Kernel k-means clustering using indices
clustInd_kmeans | K-means clustering with indices
clustInd_spc | Spectral clustering using indices
EHyClus | Clustering using Epigraph and Hypograph indices
EI | Epigraph Index (EI) for a functional dataset
generate_indices | Create a dataset with indices from a functional dataset in one or multiple dimensions
HI | Hypograph Index (HI) for a functional dataset
MEI | Modified Epigraph Index (MEI) for functional dataset.
MHI | Modified Hypograph Index (MHI) for a functional dataset
sim_model_ex1 | Function for generating functional data in one dimension
sim_model_ex2 | Function for generating functional data in one or multiple dimension