Implementation of Time-course Gene Set Analysis (TcGSA), a method for 
	analyzing longitudinal gene-expression data at the gene set level. Method is
	detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.

Methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks.  R's compatibility to simply use fast 'cpp' code via the 'Rcpp' package and the wide range spatial analysis tools via the 'terra' package makes it an attractive open source environment to study 'TCs'.  This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in 'python' by 'TCRM' <https://github.com/GeoscienceAustralia/tcrm> and then coded up in 'Cuda' 'cpp' by 'TCwindgen' <https://github.com/CyprienBosserelle/TCwindgen>.

Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.

A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.

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.