fdapace: Functional Data Analysis and Empirical Dynamics
Version 0.3.0

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".

Package details

AuthorXiongtao Dai, Pantelis Z. Hadjipantelis, Hao Ji, Hans-Georg Mueller, Jane-Ling Wang
Date of publication2017-01-25 09:02:52
MaintainerPantelis Z. Hadjipantelis <[email protected]>
LicenseBSD_3_clause + file LICENSE
URL https://github.com/functionaldata/tPACE
Package repositoryView on CRAN
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fdapace documentation built on May 29, 2017, 3:28 p.m.