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".

Documentation

Manual: fdapace.pdf
Vignette: fdapaceVignette

Maintainer: Pantelis Z. Hadjipantelis <pantelis at ucdavis.edu>

Author(s): Xiongtao Dai, Pantelis Z. Hadjipantelis, Hao Ji, Hans-Georg Mueller, Jane-Ling Wang

Install package and any missing dependencies by running this line in your R console:

install.packages("fdapace")

Depends
Imports Rcpp(>=0.11.5), Hmisc, Matrix, pracma, numDeriv
Suggests plot3D, rgl, aplpack, mgcv, ks, MASS, gtools, knitr, Rmixmod, minqa, testthat
Enhances
Linking to Rcpp, RcppEigen
Reverse
depends
Reverse
imports
Reverse
suggests
Reverse
enhances
Reverse
linking to

Package fdapace
Materials
URL https://github.com/functionaldata/tPACE
Task Views FunctionalData
Version 0.3.0
Published 2017-01-25
License BSD_3_clause + file LICENSE
BugReports https://github.com/functionaldata/tPACE/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks fdapace check results
Package source fdapace_0.3.0.tar.gz