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: 2026-10-08 12:55:57 UTC; root
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>
Config/pak/sysreqs: cmake make libgsl0-dev libicu-dev libuv1-dev
        libssl-dev
Repository: https://petersyy1677.r-universe.dev
Date/Publication: 2026-01-26 04:35:06 UTC
RemoteUrl: https://github.com/cran/TCIU
RemoteRef: HEAD
RemoteSha: bf50a78f3fd95c68e11aa9cd252c84eec92bd8cc