TCIU: Spacekime Analytics, Time Complexity and Inferential Uncertainty

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.

Package details

AuthorYongkai 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]
MaintainerYunjie Guo <jerryguo@umich.edu>
LicenseGPL-3
Version1.2.0
URL https://github.com/SOCR/TCIU https://spacekime.org https://tciu.predictive.space
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("TCIU")

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TCIU documentation built on April 30, 2021, 5:08 p.m.