DESCRIPTION

Package: PACE
Type: Package
Title: Principal Analysis by Conditional Expectation
Version: 0.2
Date: 2013-12-19
Author: Jianyang Zhao
Maintainer: Jianyang Zhao <uzhao@ucdavis.edu>
Description: PACE is a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics in Matlab. 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. 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.
License: GPL
Depends: R (>= 3.0.0), Rcpp, RcppEigen
LinkingTo: Rcpp, RcppEigen
RcppModules: PACE