squat 0.1.0

Major statistical features

• A first API proposal with a class qts and a class qts_sample for which a
  number of methods are properly implemented.
• Available statistical methods for QTS samples:
  • random generation according to the Gaussian functional model via
    rnorm_qts(),
  • scale(),
  • mean(),
  • median(),
  • distance matrix computation via distDTW() (i.e. for now we use the dynamic time warping),
  • tangent principal component analysis via prcomp(),
  • k-means with optional alignment via kmeans().
• Added multiple ways of displaying samples of QTS.
• Added two example datasets.

Improvements

• Make all functions applicable to a single QTS also applicable to QTS samples,
  with appropriate class for the output.
• Enable as_qts_sample() to generate a QTS sample of size 1 from a single QTS
  as input argument.
• Rename change_points argument to the plot.qts() function to better reflect
  its flexibility.
• Added subset operator for QTS sample objects.
• Added append S3 method for QTS sample objects.
• Added hemispherize() function to remove any discontinuities in QTS due to
  quaternion flips.
• Any parallelization computation is now handled using the
  futureverse principles and, in particular,
  implemented through the use of the furrr
  package.