Provide the core functionality to transform longitudinal data to complextime (kime) data using analytic and numerical techniques, visualize the original timeseries and reconstructed kimesurfaces, perform model based (e.g., tensorlinear regression) and modelfree 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 9783110697803. <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 timeseries, and forecast or transform the timeseries data. 2) simulate realvalued timeseries data, e.g., fMRI timecourses, detect the activated areas, report the corresponding pvalues, and visualize the pvalues in the 3D brain space. 3) Laplace transform and kimesurface reconstructions of the fMRI data.
Package details 


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 complexvalued fMRI statistics), Yunjie Guo [aut, cre], Ivo Dinov [aut] 
Maintainer  Yunjie Guo <jerryguo@umich.edu> 
License  GPL3 
Version  1.2.0 
URL  https://github.com/SOCR/TCIU https://spacekime.org https://tciu.predictive.space 
Package repository  View on CRAN 
Installation 
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