fdapace (0.5.0)

Functional Data Analysis and Empirical Dynamics.

https://github.com/functionaldata/tPACE
http://cran.r-project.org/web/packages/fdapace

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., Mller, H.G. (2016) ; Chen, K., Zhang, X., Petersen, A., Mller, H.G. (2017) .

Maintainer: Yaqing Chen
Author(s): Yaqing Chen [aut, cre], Cody Carroll [aut], Xiongtao Dai [aut], Jianing Fan [aut], Pantelis Z. Hadjipantelis [aut], Kyunghee Han [aut], Hao Ji [aut], Shu-Chin Lin [ctb], Hans-Georg Mueller [cph, ths, aut], Jane-Ling Wang [cph, ths, aut]

License: BSD_3_clause + file LICENSE

Uses: Hmisc, MASS, Matrix, numDeriv, pracma, Rcpp, aplpack, EMCluster, gtools, knitr, ks, mgcv, minqa, plot3D, rgl, testthat
Reverse depends: LCox

Released 2 months ago.