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.


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install.packages("TCIU")

1.1.0 by Yunjie Guo, a day ago


https://github.com/SOCR/TCIU, https://spacekime.org, https://tciu.predictive.space


Report a bug at https://github.com/SOCR/TCIU/issues


Browse source code at https://github.com/cran/TCIU


Authors: 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]


Documentation:   PDF Manual  


GPL-3 license


Imports stats, ggplot2, dplyr, tidyr, RColorBrewer, fancycut, scales, plotly, gridExtra, ggpubr, ICSNP, AnalyzeFMRI, rrcov, geometry, DT, forecast, fmri, pracma, zoo, extraDistr, parallel, foreach, spatstat, cubature, doParallel, reshape2, MultiwayRegression

Suggests oro.nifti, magrittr, knitr, rmarkdown, webshot

System requirements: GNU make


See at CRAN