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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

• Add DBSCAN clustering;
• Fix C++ compiler issues that errored when accessing empty vectors.

fdacluster 0.2.0

• 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.
• 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.

fdacluster 0.1.0

• Initial release.
• Added a NEWS.md file to track changes to the package.

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.
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