Skip to content
Data Science, Time Complexity and Inferential Uncertainty (TCIU)
HTML R
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
TCIU_package_content
TSplotly
code
images
README.md

README.md

TCIU

Data Science, Time Complexity and Inferential Uncertainty (TCIU)

Table of contents

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. See the code folder.

Team

SOCR Team including Ivo D. Dinov, Milen V. Velev, Yongkai Qiu, Zhe Yin, Yufei Yang, and others.

Acknowledgments

This work is supported in part by NIH grants P20 NR015331, P50 NS091856, P30 DK089503, P30AG053760, 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

You can’t perform that action at this time.