fdapace - Functional Data Analysis and Empirical Dynamics
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. Updates to
fdapace were supported by grants from NIH Echo and NSF
DMS-1712864 and DMS-2014626. Please cite our package if you use
it (You may run the command 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>; Chen, K.,
Zhang, X., Petersen, A., Müller, H.G. (2017)
<doi:10.1007/s12561-015-9137-5>.