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sasfunclust: Sparse and Smooth Functional Clustering

Implements the sparse and smooth functional clustering (SaS-Funclust) method (Centofanti et al. (2021) <doi:10.48550/arXiv.2103.15224>) that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of domain.

Version: 1.0.0
Imports: Rcpp, fda, mclust, matrixcalc, MASS, Matrix
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat
Published: 2021-04-02
DOI: 10.32614/CRAN.package.sasfunclust
Author: Fabio Centofanti [cre, aut], Antonio Lepore [aut], Biagio Palumbo [aut]
Maintainer: Fabio Centofanti <fabio.centofanti at unina.it>
BugReports: https://github.com/unina-sfere/sasfunclust/issues
License: GPL-3
URL: https://github.com/unina-sfere/sasfunclust
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README
In views: FunctionalData
CRAN checks: sasfunclust results

Documentation:

Reference manual: sasfunclust.pdf

Downloads:

Package source: sasfunclust_1.0.0.tar.gz
Windows binaries: r-devel: sasfunclust_1.0.0.zip, r-release: sasfunclust_1.0.0.zip, r-oldrel: sasfunclust_1.0.0.zip
macOS binaries: r-release (arm64): sasfunclust_1.0.0.tgz, r-oldrel (arm64): sasfunclust_1.0.0.tgz, r-release (x86_64): sasfunclust_1.0.0.tgz, r-oldrel (x86_64): sasfunclust_1.0.0.tgz

Linking:

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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.