Provides implementation of various methods of Functional Data
Analysis (FDA) and Empirical Dynamics. The core of this package is Functional
Principal Component Analysis (FPCA), a key technique for functional data
analysis, for sparsely or densely sampled random trajectories and time courses,
via the Principal Analysis by Conditional Estimation (PACE) algorithm or
numerical integration. PACE is useful for the analysis of data that have been
generated by a sample of underlying (but usually not fully observed) random
trajectories. It does not rely on pre-smoothing of trajectories, which is
problematic if functional data are sparsely sampled. PACE provides options
for functional regression and correlation, for Longitudinal Data Analysis,
the analysis of stochastic processes from samples of realized trajectories,
and for the analysis of underlying dynamics. The core computational algorithms
are implemented using the 'Eigen' C++ library for numerical linear algebra and
'RcppEigen' "glue".
| Version: |
0.4.1 |
| Imports: |
Rcpp (≥ 0.11.5), Hmisc, MASS, Matrix, pracma, numDeriv |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
plot3D, rgl, aplpack, mgcv, ks, gtools, knitr, EMCluster, minqa, testthat |
| Published: |
2019-03-19 |
| Author: |
Xiongtao Dai [aut],
Pantelis Z. Hadjipantelis [aut, cre],
Kynghee Han [aut],
Hao Ji [aut],
Shu-Chin Lin [ctb],
Hans-Georg Mueller [cph, ths],
Jane-Ling Wang [cph, ths] |
| Maintainer: |
Pantelis Z. Hadjipantelis <pantelis at ucdavis.edu> |
| BugReports: |
https://github.com/functionaldata/tPACE/issues |
| License: |
BSD_3_clause + file LICENSE |
| URL: |
https://github.com/functionaldata/tPACE |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| In views: |
FunctionalData |
| CRAN checks: |
fdapace results |