Package: fdapace
Type: Package
Title: Functional Data Analysis and Empirical Dynamics
URL: https://github.com/functionaldata/tPACE
BugReports: https://github.com/functionaldata/tPACE/issues
Version: 0.6.0
Encoding: UTF-8
Date: 2024-07-02
Language: en-US
Authors@R: c(person("Yidong", "Zhou", email="ydzhou@ucdavis.edu", role=c("cre","aut"), comment=c(ORCID="0000-0003-1423-1857")),
    person("Han", "Chen", role=c("aut")),
    person("Su I", "Iao", role=c("aut")),
    person("Poorbita", "Kundu", role=c("aut")),
    person("Hang", "Zhou", role=c("aut")),
    person("Satarupa","Bhattacharjee", role=c("aut")),
    person("Cody","Carroll", role=c("aut"), comment=c(ORCID="0000-0003-3525-8653")),
    person("Yaqing","Chen", role=c("aut")),
    person("Xiongtao","Dai", role=c("aut")),
    person("Jianing","Fan", role=c("aut")),
    person("Alvaro","Gajardo", role=c("aut")),
    person("Pantelis Z.","Hadjipantelis", role=c("aut")),
    person("Kyunghee","Han", role="aut"),
    person("Hao","Ji", role=c("aut")),
    person("Changbo","Zhu", role=c("aut")),
    person("Paromita", "Dubey", role="ctb"),
    person("Shu-Chin", "Lin", role="ctb"),
    person("Hans-Georg", "Müller", role=c("cph","ths","aut")),
    person("Jane-Ling", "Wang", role=c("cph","ths","aut"))) 
Maintainer: Yidong Zhou <ydzhou@ucdavis.edu>
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. 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>.
License: BSD_3_clause + file LICENSE
LazyData: false
Imports: Rcpp (>= 0.11.5), Hmisc, MASS, Matrix, pracma, numDeriv
LinkingTo: Rcpp, RcppEigen
Suggests: plot3D, rgl, aplpack, mgcv, ks, gtools, knitr, rmarkdown,
        EMCluster, minqa, testthat
NeedsCompilation: yes
RoxygenNote: 7.3.1
VignetteBuilder: knitr
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev
Repository: https://functionaldata.r-universe.dev
Date/Publication: 2024-07-03 05:37:09 UTC
RemoteUrl: https://github.com/functionaldata/tpace
RemoteRef: HEAD
RemoteSha: e778562f5f253ae4146d6cfae5df35514d596a75
Packaged: 2026-06-24 09:45:22 UTC; root
Author: Yidong Zhou [cre, aut] (ORCID: <https://orcid.org/0000-0003-1423-1857>),
  Han Chen [aut],
  Su I Iao [aut],
  Poorbita Kundu [aut],
  Hang Zhou [aut],
  Satarupa Bhattacharjee [aut],
  Cody Carroll [aut] (ORCID: <https://orcid.org/0000-0003-3525-8653>),
  Yaqing Chen [aut],
  Xiongtao Dai [aut],
  Jianing Fan [aut],
  Alvaro Gajardo [aut],
  Pantelis Z. Hadjipantelis [aut],
  Kyunghee Han [aut],
  Hao Ji [aut],
  Changbo Zhu [aut],
  Paromita Dubey [ctb],
  Shu-Chin Lin [ctb],
  Hans-Georg Müller [cph, ths, aut],
  Jane-Ling Wang [cph, ths, aut]