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Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.48550/arXiv.2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.
| Version: | 2.0 |
| Depends: | R (≥ 2.10) |
| Imports: | kader, utils, pracma, fdapace, fda, stats, graphics |
| Published: | 2022-02-04 |
| Author: | Rou Zhong [aut, cre], Jingxiao Zhang [aut] |
| Maintainer: | Rou Zhong <zhong_rou at 163.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | KFPCA results |
| Reference manual: | KFPCA.pdf |
| Package source: | KFPCA_2.0.tar.gz |
| Windows binaries: | r-devel: KFPCA_2.0.zip, r-release: KFPCA_2.0.zip, r-oldrel: KFPCA_2.0.zip |
| macOS binaries: | r-release (arm64): KFPCA_2.0.tgz, r-oldrel (arm64): KFPCA_2.0.tgz, r-release (x86_64): KFPCA_2.0.tgz, r-oldrel (x86_64): KFPCA_2.0.tgz |
| Old sources: | KFPCA archive |
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