DESCRIPTION

Package: tPACE
Type: Package
Title: PACE package for Functional Data Analysis and Empirical Dynamics
URL: https://github.com/hadjipantelis/tPACE
Version: 0.0.1.0000
Date: 2015-11-29
Author: Xiongtao Dai,
    Pantelis Z. Hadjipantelis,
    Hao Ji,
    Hans-Georg Mueller,
    Jane-Ling Wang
Maintainer: Pantelis Z. Hadjipantelis <pantelis@ucdavis.edu>
Description: PACE is a versatile package that 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. 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.
Depends:
    R (>= 3.1.0)
License: BSD_3_clause
LazyData: false
Imports:
    Rcpp (>= 0.11.5),
    RcppEigen,
    gtools,
    Hmisc,
    plot3D,
    MASS,
    Matrix,
    pracma,
    numDeriv
LinkingTo: Rcpp, RcppEigen
Suggests:
    rgl,
    aplpack,
    mgcv,
    ks
NeedsCompilation: yes
RoxygenNote: 5.0.1