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TCIU
View on CRAN: Click
here
Download and install TCIU package within the R console
Install from CRAN:
install.packages("TCIU")
Install from Github:
library("remotes")
install_github("cran/TCIU")
Install by package version:
library("remotes")
install_version("TCIU", "1.2.6")
Attach the package and use:
library("TCIU")
Maintained by
Yueyang Shen
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-08-27
Latest Update: 2023-05-04
Description:
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 Functional magnetic resonance imaging(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.
How to cite:
Yueyang Shen (2020). TCIU: Spacekime Analytics, Time Complexity and Inferential Uncertainty. R package version 1.2.6, https://cran.r-project.org/web/packages/TCIU
Previous versions and publish date:
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