fdapace (0.4.1)

0 users

Functional Data Analysis and Empirical Dynamics.


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 'RcppEigen' "glue".

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

License: BSD_3_clause + file LICENSE

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

Released about 1 month ago.

6 previous versions



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of fdapace yet. Want to be the first? Write one now.

Related packages: fda, fpca, rainbow, fds, ftsa, geofd, refund, fda.usc, dbstats, fdasrvf, ddalpha, switchnpreg, fdatest, fdakma, GPFDA, FDboost, growfunctions, funHDDC, funFEM, RFgroove(20 best matches, based on common tags.)

Search for fdapace on google, google scholar, r-help, r-devel.

Visit fdapace on R Graphical Manual.