Functions to delineate temporal dataset shifts in Electronic Health 
             Records through the projection and visualization of dissimilarities 
             among data temporal batches. This is done through the estimation of 
             data statistical distributions over time and their projection in 
             non-parametric statistical manifolds, uncovering the patterns of the 
             data latent temporal variability. 'EHRtemporalVariability' is 
             particularly suitable for multi-modal data and categorical variables 
             with a high number of values, common features of biomedical data where 
             traditional statistical process control or time-series methods may not 
             be appropriate. 'EHRtemporalVariability' allows you to explore and 
             identify dataset shifts through visual analytics formats such as 
             Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. 
             An additional 'EHRtemporalVariability' Shiny app can be used to load 
             and explore the package results and even to allow the use of these 
             functions to those users non-experienced in R coding. (Sáez et al. 2020)
             <doi:10.1093/gigascience/giaa079>.

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.