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An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).
| Version: | 1.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | MFPCA, cluster, fpc, fda, fda.usc, funData, stats, graphics |
| Published: | 2022-11-07 |
| DOI: | 10.32614/CRAN.package.FADPclust |
| Author: | Rui Ren [aut, cre], Kuangnan Fang [aut], Qingzhao Zhang [aut], Xiaofeng Wang [aut] |
| Maintainer: | Rui Ren <xmurr at stu.xmu.edu.cn> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | FADPclust results |
| Reference manual: | FADPclust.pdf |
| Package source: | FADPclust_1.1.1.tar.gz |
| Windows binaries: | r-devel: FADPclust_1.1.1.zip, r-release: FADPclust_1.1.1.zip, r-oldrel: FADPclust_1.1.1.zip |
| macOS binaries: | r-release (arm64): FADPclust_1.1.1.tgz, r-oldrel (arm64): FADPclust_1.1.1.tgz, r-release (x86_64): FADPclust_1.1.1.tgz, r-oldrel (x86_64): FADPclust_1.1.1.tgz |
| Old sources: | FADPclust archive |
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