fdacluster 0.4.0
CRAN release: 2025-01-12
Major features
- Expanded arguments of 
fdakmeans()to allow for more control over the type of input functional data:- 
is_domain_intervalallows one to state if all curves are defined on the same fixed interval; - 
transformationspecifies 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 distance and normalized 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 
READMEfile. - Updated GHA workflows.
 - Updated vignettes.
 - Bug fixes.
 
fdacluster 0.3.0
CRAN release: 2023-07-04
- Added median centroid type;
 - Median and mean centroid types are now defined on the union of individual grids;
 - Simplified 
capsclass 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
CRAN release: 2023-05-25
- 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
CRAN release: 2023-05-19
- Add DBSCAN clustering;
 - Fix C++ compiler issues that errored when accessing empty vectors.
 
fdacluster 0.2.0
CRAN release: 2023-05-18
- Add hierarchical clustering;
 - Enforce 
n_clustersin output via linear programming (LP) using the lpSolve package; - New 
capsclass for storing results from functional Clustering with Amplitude and Phase Separation in a consistent way; - Add tools for comparing clustering results (
mcapsobjects,autoplotandplotspecialized 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 usefdahclust(). 
