Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.

Functions to represent functional objects under a Reproducing Kernel Hilbert Space (RKHS) framework as described 
  in Muñoz & González (2010). Autoregressive Hilbertian Model for functional time series using RKHS and predictive confidence bands construction 
  as proposed in Hernández et al (2021).

Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    'The R Inferno' <https://www.burns-stat.com/pages/Tutor/R_inferno.pdf>).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.

Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional data sets.

Tools to help text files importation. It can return 
 the number of lines; print the first and last lines; convert 
 encoding. Operations are made without reading the entire file 
 before starting, resulting in good performances with large files. 
 This package provides an alternative to a simple use of the 
 'head', 'tail', 'wc' and 'iconv' programs that are not always 
 available on machine where R is installed.