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Version 2.0.1

• Fixed bug in function cost_matrix_diagonal_weighted_cpp() where the additional weight of the diagonal movement was not being correctly applied. This change will result in slightly different psi values in distantia(), distantia_dtw(), and distantia_dtw_plot() when diagonal = TRUE (default).

• Fixed bug in function cost_path_cpp, which still produced diagonal cost matrices when diagonal = FALSE because weighted = TRUE turned diagonal to TRUE. Now weighted is set to FALSE when diagonal = FALSE. This resulted in negative scores for orthogonal least-cost paths.

• All C++ functions returning values of type double to R functions now round their output to the 8th decimal. This should mitigate discrepancies between R and C++ functions due to differences in how these systems round floating point numbers.

Version 2.0.0

• This new version involves a massive rewrite that will break any previous code based on this package. To install the previous version (1.0.2):

#install from CRAN archive
remotes::install_version(
  package = "distantia", 
  version = "1.0.2"
  )

#install from archive branch in GitHub
remotes::install_github(
  repo = "https://github.com/BlasBenito/distantia",
  ref = "v1.0.2"
  )

• Version 2.0.0 is a complete package rewrite from the ground up:

  • All core functions have been rewritten in C++ for increased speed and memory efficiency, and proper R wrappers for these functions are provided.

  • All functions and their arguments follow more modern naming conventions, and simplified interfaces to improve the user experience.

  • Most time series operations use the zoo library underneath, ensuring data consistency, computational speed, and memory efficiency.

  • Lists of zoo objects, named “time series lists” (“tsl” for short) throughout the package documentation, are used to organize time series data.

  • A complete toolset to manage time series lists is provided. All functions belonging are named using the prefix tsl_...(). There are tools to generate, aggregate, resample, transform, plot, map, and analyze univariate or multivariate regular or irregular time series.

  • Most functions taking time series lists as inputs are parallelized using the future package, and progress bars for parallelized operations are available as well via the progressr package.

  • New example datasets from different disciplines and functions to generate simulated time series are shipped with the package to improve the learning experience.

Version 1.0.3

Fixed bug in Hellinger distances and reworked the distance() function to make it slightly faster.

Version 1.0.2

Fixed an issue with the parallelization of tasks in the Windows platform. Now all parallelized functions modify their cluster settings depending on the user’s platform.

Version 1.0.1

Fixed a bug in the function workflowImportance. The argument ‘exclude.columns’ was being ignored.

Fixed the documentation of the functions workflowImportance and workflowSlotting. Their outputs were not well documented.

Fixed an error in workflowTransfer.

Changed how psi is computed. It’s now more respectful with the original formulation, and handles very similar sequences better.

Fixed the function workflowPsi to add +1 to the least cost produced by the options paired.samples = TRUE and diagonal = TRUE

Added the function workflowPsiHP, a High Performance version of workflowPsi. It has less options, but it is much faster, and has a much lower memory footprint.

Version 1.0.0 is ready!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.