TCIU

Data Science, Time Complexity and Inferential Uncertainty (TCIU)

Table of contents

• Table of contents
• Overview
• R Code
• Python Code
• Team
• Acknowledgments
• References

Overview

The SOCR Data Science Fundamentals project explores new theoretical representation and analytical strategies to understand large and complex data, time complexity and inferential uncertainty. It utilizes information measures, entropy KL divergence, PDEs, Dirac’s bra-ket operators (〈 , 〉). This fundamentals of data science research project will explore time-complexity and inferential uncertainty in modeling, analysis and interpretation of large, heterogeneous, multi-source, multi-scale, incomplete, incongruent, and longitudinal data.

R Code

The examples, demonstrations and simulations are designed, built, implemented and validated in the R environment.

The source R code for the package is in the package (TCIU) folder.

The source RMarkdown code for the website is in the website folder.

To interactively run of the demo code, following a TCIU package installation, use the following command in the R console/shell:

library("TCIU")
demo(fmri_demo_func, package="TCIU")

Python Code

An early (pilot) version of a Python implementaiton of the TCIU package will be available in the package (TCIU_python) folder. This is still under development and valudation ...

Team

SOCR Team including Ivo D. Dinov, Milen V. Velev, Yongkai Qiu, Zhe Yin, Yufei Yang, Yunjie Guo, Yupeng Zhang, Rongqian Zhang, Yuyao Liu, Jinwen Cao, Zijing Li, Daxuan Deng, Yueyang Shen, and others.

Acknowledgments

This work is supported in part by NIH grants P20 NR015331, UL1TR002240, P30 DK089503, UL1TR002240, and NSF grants 1916425, 1734853, 1636840, 1416953, 0716055 and 1023115. Students, trainees, scholars, and researchers from SOCR, BDDS, MNORC, MIDAS, MADC, MICHR, and the broad R-statistical computing community have contributed ideas, code, and support.

References

• Dinov, ID and Velev, MV (2021) Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics, De Gruyter STEM Series, Berlin/Boston, ISBN 9783110697803 / 3110697807.
• Wang, Y, Shen Y, Deng, D, Dinov, ID. (2022) Determinism, Well-posedness, and Applications of the Ultrahyperbolic Wave Equation in Spacekime, Journal of Partial Differential Equations in Applied Mathematics, DOI: 10.1016/j.padiff.2022.100280, in press.
• Zhang, R, Zhang, Y, Liu, Y, Guo, Y, Shen, Y, Deng, D, Qiu, Y, Dinov, ID. (2022) Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data, Neural Computing and Applications Journal, DOI: 10.1007/s00521-021-06789-8, in press.
• TCIU Website.
• Spacekime.
• TSPlotly package on CRAN.
• TCIU package on CRAN.