Package: fdacluster 0.3.0.9000

fdacluster: Joint Clustering and Alignment of Functional Data

Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRSF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRSF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.

Authors:Aymeric Stamm [aut, cre], Laura Sangalli [ctb], Piercesare Secchi [ctb], Simone Vantini [ctb], Valeria Vitelli [ctb], Alessandro Zito [ctb]

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fdacluster.pdf |fdacluster.html
fdacluster/json (API)
NEWS

# Install 'fdacluster' in R:
install.packages('fdacluster', repos = c('https://astamm.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/astamm/fdacluster/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

9 exports 5 stars 1.65 score 94 dependencies 1 dependents 24 scripts 314 downloads

Last updated 7 months agofrom:0cecb99115. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 22 2024
R-4.5-win-x86_64OKAug 22 2024
R-4.5-linux-x86_64OKAug 22 2024
R-4.4-win-x86_64OKAug 22 2024
R-4.4-mac-x86_64OKAug 22 2024
R-4.4-mac-aarch64OKAug 22 2024
R-4.3-win-x86_64OKAug 22 2024
R-4.3-mac-x86_64OKAug 22 2024
R-4.3-mac-aarch64OKAug 22 2024

Exports:as_capscompare_capsdiagnostic_plotfdadbscanfdadistfdahclustfdakmeansis_capslp

Dependencies:askpassbase64encbslibcachemcliclustercodacodetoolscolorspacecpp11crosstalkcurldata.tabledbscandigestdoParalleldotCall64dplyrevaluatefansifarverfastmapfdasrvffieldsfontawesomeforcatsforeachfsfurrrfuturegenericsggplot2globalsgluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelistenvlpSolvemagrittrmapsMASSMatrixmemoisemgcvmimemunsellmvtnormnlmenloptropensslparallellypillarpkgconfigplotlyprogressrpromisespurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangrmarkdownsassscalesspamstringistringrsystibbletidyrtidyselecttinytextoleranceutf8vctrsviridisLitewithrxfunyaml

Analysis of Berkeley growth data

Rendered fromberkeley-growth.Rmdusingknitr::rmarkdownon Aug 22 2024.

Last update: 2024-01-09
Started: 2023-06-26

Computing initial centroids in k-means

Rendered fromkmeans-initialisation.Rmdusingknitr::rmarkdownon Aug 22 2024.

Last update: 2024-01-09
Started: 2023-06-24