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 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.
|Author||Yongkai Qiu [aut], Zhe Yin [aut], Jinwen Cao [aut], Yupeng Zhang [aut], Yuyao Liu [aut], Rongqian Zhang [aut], Rouben Rostamian [ctb], Ranjan Maitra [ctb], Daniel Rowe [ctb], Daniel Adrian [ctb] (gLRT method for complex-valued fMRI statistics), Yunjie Guo [aut, cre], Ivo Dinov [aut]|
|Maintainer||Yunjie Guo <email@example.com>|
|URL||https://github.com/SOCR/TCIU https://spacekime.org https://tciu.predictive.space|
|Package repository||View on CRAN|
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