Imports: Rcpp (>= 0.11.5), Hmisc, Matrix, pracma, numDeriv
Suggests: plot3D, rgl, aplpack, mgcv, ks, MASS, gtools, knitr, Rmixmod,

Description: 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
Imports: Rcpp (>= 0.11.5), Hmisc, MASS, Matrix, pracma, numDeriv
Suggests: plot3D, rgl, aplpack, mgcv, ks, gtools, knitr, EMCluster,

Description: 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. This core algorithm yields covariance and mean functions,
eigenfunctions and principal component (scores), for both functional data and
derivatives, for both dense (functional) and sparse (longitudinal) sampling designs.
For sparse designs, it provides fitted continuous trajectories with confidence bands,
even for subjects with very few longitudinal observations. PACE is a viable and
flexible alternative to random effects modeling of longitudinal data. There is also a
Matlab version (PACE) that contains some methods not available on fdapace and vice
versa. Please cite our package if you use it (You may run the command
devtools::citation("fdapace") to get the citation format and bibtex entry).
References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>;