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The proposed method aims at predicting the longitudinal mean response trajectory by a kernel-based estimator. The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels.
| Version: | 0.1.0 |
| Imports: | tidyr, bvls, fdapace, mvtnorm, dplyr, purrr |
| Published: | 2023-07-07 |
| DOI: | 10.32614/CRAN.package.longke |
| Author: | Shixuan Wang [aut, cre], Seonjin Kim [aut], Hyunkeun Cho [aut], Won Chang [aut] |
| Maintainer: | Shixuan Wang <wangs43 at miamioh.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | longke results |
| Reference manual: | longke.pdf |
| Package source: | longke_0.1.0.tar.gz |
| Windows binaries: | r-devel: longke_0.1.0.zip, r-release: longke_0.1.0.zip, r-oldrel: longke_0.1.0.zip |
| macOS binaries: | r-release (arm64): longke_0.1.0.tgz, r-oldrel (arm64): longke_0.1.0.tgz, r-release (x86_64): longke_0.1.0.tgz, r-oldrel (x86_64): longke_0.1.0.tgz |
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