fdacluster 0.4.1

Bug fixes

• Properly set up future workers by ensuring that fdacluster is loaded.
• Replace SRSF acronym with the correct SRVF one.

fdacluster 0.4.0

Major features

• Expanded arguments of fdakmeans() to allow for more control over the type of
  input functional data:
  • is_domain_interval allows one to state if all curves are defined on the
    same fixed interval;
  • transformation specifies the transformation to be applied to the data
    before clustering.
  • check_option_compatibility() handles errors when incompatible
    options are selected.
• Created two separate C++ classes for $L^2$ distance and normalized $L^2$
  distance; the former cannot be used in combination with dilation or affine
  warping classes because it is not invariant to these transformations.

Minor improvements and bug fixes

• Integrated distances in C++ classes are now computed via arma::trapz().
• Added talk given at Rencontres R 2023 in Avignon, France to the News section
  of the website.
• Reduced number of dependencies: removed dplyr, forcats, tidyr, purrr.
• Replaced furrr dependency in favor of future.apply to further reduce number of
  dependencies.
• Updated README file.
• Updated GHA workflows.
• Updated vignettes.
• Bug fixes.

fdacluster 0.3.0

• Added median centroid type;
• Median and mean centroid types are now defined on the union of individual grids;
• Simplified caps class to avoid storing objects multiple times under different names;
• Added vignette on initialization strategies for k-means;
• Added article on use case about the Berkeley growth study;
• Added article on supported input formats.

fdacluster 0.2.2

• Make sure one can use fdacluster with namespace notation.
• Make sure not to use fda or funData before checking it is available.

fdacluster 0.2.1

Major features

• Add hierarchical clustering;
• Enforce n_clusters in output via linear programming (LP) using the
  lpSolve package;
• New caps class
  for storing results from functional Clustering with Amplitude and
  Phase Separation in a consistent way;
• Add tools for comparing clustering results (mcaps objects, autoplot and
  plot specialized method implementations);
• Add seeding strategies for kmeans (via hierarchical clustering or k-means++ or
  k-means++ with exhaustive search of the first center or exhaustive search of all
  the centers);
• Add within-cluster domain auto-extension via mean imputation;
• Add possibility to cluster according to phase variability instead of amplitude
  variability.
• Add DBSCAN clustering.

Minor improvements

• Fix C++ compiler issues that errored when accessing empty vectors.
• Renaming of functions: to perform k-means with alignment, now use
  fdakmeans(),
  to perform HAC with alignment, now use
  fdahclust().

fdacluster 0.1.1

• Fixed undefined behavior sanitizer issues spotted by UBSAN.
• Added reference to published work related to the package in DESCRIPTION.