Description: The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/web_api.jsp> for more information). Features of 'TCGAretriever' include: 1) it is very simple to use (get all the TCGA data you need with a few lines of code); 2) performance (smooth and reliable data download via 'httr'); 3) it is tailored for downloading large volumes of data.

Description: The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/> for more information). 'TCGAretriever' is easy to use (get all the TCGA data you need with a few lines of code), enforces reliable data download (via 'httr'), and is suitable for downloading large volumes of data.

Title: Client for the Cancer Imaging Archive REST API
Authors@R: person("Pamela", "Russell", email = "pamela.russell@ucdenver.edu", role = c("aut", "cre"))
Description: 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>

Suggests: knitr, rmarkdown, oro.dicom, radiomics

Suggests: knitr, rmarkdown, oro.dicom, testthat

Authors@R: person("Pamela", "Russell", email = "pamela.russell@gmail.com", role = c("aut", "cre"))

Packaged: 2019-09-21 11:06:07 UTC; pamela.russell

Title: Spacekime Analytics, Time Complexity and Inferential Uncertainty
Authors@R: c(person("Yongkai", "Qiu", role = "aut", email = "yongkai@umich.edu"),
person("Yupeng", "Zhang", role = "aut"),
person("Ranjan", "Maitra", role = "ctb"),
person("Daniel", "Adrian", role = "ctb", comment = "gLRT method for complex-valued fMRI statistics"),
person("Yunjie", "Guo", role = c("aut", "cre"), email = "jerryguo@umich.edu"),
person("Ivo","Dinov", role = "aut", email = "statistics@umich.edu"))
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,
The package includes 18 core functions which can be separated into three groups.
1) draw longitudinal data, such as 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.
Imports: stats, ggplot2, dplyr, tidyr, RColorBrewer, fancycut, scales,
plotly, gridExtra, ggpubr, ICSNP, AnalyzeFMRI, rrcov, geometry,
DT, forecast, fmri, pracma, zoo, extraDistr, parallel, foreach,
spatstat, cubature, doParallel, reshape2, MultiwayRegression
Daniel Adrian [ctb] (gLRT method for complex-valued fMRI statistics),