mask_dict

<p>a data.frame containing the label index corresponding to its label name</p>

data.frame

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.

Yueyang Shen

TCIU

Spacekime Analytics, Time Complexity and Inferential Uncertainty

Yongkai Qiu

Zhe Yin

Jinwen Cao

Yupeng Zhang

Yuyao Liu

Rongqian Zhang

Rouben Rostamian

Ranjan Maitra

Daniel Rowe

Daniel Adrian

Yunjie Guo

Ivo Dinov

mask_dict function

Format

mask_dict — mask_dict

mask_dict: mask_dict

Description

Usage

Arguments

Format