Package: TCIU
Title: Spacekime Analytics, Time Complexity and Inferential Uncertainty
Version: 1.2.8
Authors@R: c(person("Yongkai", "Qiu", role = "aut", email = "yongkai@umich.edu"),
             person("Zhe", "Yin", role = "aut"),
             person("Jinwen", "Cao", role = "aut"),
             person("Yupeng", "Zhang", role = "aut"),
             person("Yuyao", "Liu", role = "aut"),
             person("Rongqian", "Zhang", role = "aut"),
             person("Yueyang", "Shen", role = c("aut", "cre"), email = "petersyy@umich.edu"),
             person("Rouben", "Rostamian", role = "ctb"),
             person("Ranjan", "Maitra", role = "ctb"),
             person("Daniel", "Rowe", role = "ctb"),
             person("Daniel", "Adrian", role = "ctb", comment = "gLRT method for complex-valued fMRI statistics"),
             person("Yunjie", "Guo", role = "aut", email = "jerryguo@umich.edu"),
             person("Ivo","Dinov", role = "aut", email = "statistics@umich.edu"))
URL: https://github.com/SOCR/TCIU,
        https://www.socr.umich.edu/spacekime/,
        https://www.socr.umich.edu/TCIU/
BugReports: https://github.com/SOCR/TCIU/issues
Description: Provide the core functionality to transform longitudinal data to
    complex-time (kime) data using analytic and numerical techniques, visualize the original 
    time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression)
    and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021)
    "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series,
    ISBN 978-3-11-069780-3. <https://www.degruyter.com/view/title/576646>.
    The package includes 18 core functions which can be separated into three groups.
    1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data.
    2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas,
    report the corresponding p-values, and visualize the p-values in the 3D brain space.
    3) Laplace transform and kimesurface reconstructions of the fMRI data.
Depends: R (>= 3.5.0)
Imports: stats, ggplot2, dplyr, tidyr, RColorBrewer, fancycut, scales,
        plotly, gridExtra, ggpubr, ICSNP, rrcov, geometry, DT,
        forecast, fmri, pracma, zoo, extraDistr, parallel, foreach,
        spatstat.explore, spatstat.geom, cubature, doParallel,
        reshape2, MultiwayRegression, interp
Suggests: oro.nifti, magrittr, knitr, rmarkdown
License: GPL-3
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: yes
SystemRequirements: GNU make
Packaged: 2026-01-26 01:32:07 UTC; peter
Author: Yongkai Qiu [aut],
  Zhe Yin [aut],
  Jinwen Cao [aut],
  Yupeng Zhang [aut],
  Yuyao Liu [aut],
  Rongqian Zhang [aut],
  Yueyang Shen [aut, cre],
  Rouben Rostamian [ctb],
  Ranjan Maitra [ctb],
  Daniel Rowe [ctb],
  Daniel Adrian [ctb] (gLRT method for complex-valued fMRI statistics),
  Yunjie Guo [aut],
  Ivo Dinov [aut]
Maintainer: Yueyang Shen <petersyy@umich.edu>
Repository: CRAN
Date/Publication: 2026-01-26 03:30:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 14:15:55 UTC; unix
Archs: TCIU.so.dSYM
