Description | Load package | Data | Example of Clustering univariate functional data | Basic code example | Selection of the number of clusters | Example of clustering bivariate functional data | Model selection
Package: funHDDC 2.3.1.1
funHDDC: Univariate and Multivariate Model-Based Clustering in Group-Specific Functional Subspaces
The funHDDC algorithm allows to cluster functional univariate (Bouveyron and Jacques, 2011, <doi:10.1007/s11634-011-0095-6>) or multivariate data (Schmutz et al., 2018) by modeling each group within a specific functional subspace.
Authors:
funHDDC_2.3.1.1.tar.gz
funHDDC_2.3.1.1.zip(r-4.7-any)funHDDC_2.3.1.1.zip(r-4.6-any)funHDDC_2.3.1.1.zip(r-4.5-any)
funHDDC_2.3.1.1.tgz(r-4.6-any)funHDDC_2.3.1.1.tgz(r-4.5-any)
funHDDC_2.3.1.1.tar.gz(r-4.7-any)funHDDC_2.3.1.1.tar.gz(r-4.6-any)
funHDDC_2.3.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
funHDDC/json (API)
| # Install 'funHDDC' in R: |
| install.packages('funHDDC', repos = c('https://jujacques.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:ffa28c18b4. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 146 | ||
| source / vignettes | OK | 195 | ||
| linux-release | OK | 144 | ||
| macos-release | OK | 113 | ||
| macos-oldrel | OK | 103 | ||
| windows-devel | OK | 107 | ||
| windows-release | OK | 77 | ||
| windows-oldrel | OK | 79 | ||
| wasm-release | OK | 98 |
Exports:funHDDCmfpcaplot.fdplot.mfpcapredict.funHDDCslopeHeuristic
Dependencies:ashbitopscliclustercolorspacecpp11deSolvefarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmaR6rainbowRColorBrewerRcppRCurlrlangS7scalesvctrsviridisLitewithr
