fdapace: Functional Data Analysis and Empirical Dynamics

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: 0.3.0
Imports: Rcpp (≥ 0.11.5), Hmisc, Matrix, pracma, numDeriv
LinkingTo: Rcpp, RcppEigen
Suggests: plot3D, rgl, aplpack, mgcv, ks, MASS, gtools, knitr, Rmixmod, minqa, testthat
Published: 2017-01-25
Author: Xiongtao Dai, Pantelis Z. Hadjipantelis, Hao Ji, Hans-Georg Mueller, Jane-Ling Wang
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
CRAN checks: fdapace results

Downloads:

Reference manual: fdapace.pdf
Vignettes: fdapaceVignette
Package source: fdapace_0.3.0.tar.gz
Windows binaries: r-devel: fdapace_0.3.0.zip, r-release: fdapace_0.3.0.zip, r-oldrel: fdapace_0.3.0.zip
OS X Mavericks binaries: r-release: fdapace_0.3.0.tgz, r-oldrel: fdapace_0.3.0.tgz
Old sources: fdapace archive

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