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.