Reads in Neuroimaging 'GIFTI' Files with Geometry Information
Functions to read in the geometry format under the
Neuroimaging Informatics Technology Initiative ('NIfTI'), called
'GIFTI' <https://www.nitrc.org/projects/gifti/>.
These files contain surfaces of brain imaging data.
rgl, grDevices, testthat, knitr, rmarkdown

Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information
Functions to read in the geometry format under the
'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called
'GIFTI' <https://www.nitrc.org/projects/gifti/>.
These files contain surfaces of brain imaging data.
xml2 (>= 1.1.1), base64enc, R.utils, tools
rgl, grDevices, testthat, knitr, rmarkdown, covr

xml2 (>= 1.1.1), base64enc, R.utils, tools, utils

Take Shell Commands and Turn Them into R Functions
c(person(given = "Chris",
family = "Cardillo",
role = c("aut", "cre"),
email = "cfcardillo23@gmail.com"),
person(given = "David",
family = "Robinson",
role = "ctb",
email = "admiral.david@gmail.com"))
Wrapping command line functions into R functions, which in turn run the command line functions.
namespace, processx, utils, readr, tibble
Chris Cardillo [aut, cre],
David Robinson [ctb]

Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with
Grouping Structure
A Gibbs sampler corresponding to a Group
Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates.
Hyperparameters in the GIGG prior specification can either be fixed by the
user or can be estimated via Marginal Maximum Likelihood Estimation.
Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar
Mukherjee (2021) <arXiv:2102.10670>.
c(
person(given = "Jon", family = "Boss", role = c("aut"), email = "gutian@umich.edu"),
person(given = "Bhramar", family = "Mukherjee", role = c("aut"), email = "bhramar@umich.edu"),
person(given = "Michael", family = "Kleinsasser", role = c("cre"), email = "mkleinsa@umich.edu")
)
Jon Boss [aut],
Bhramar Mukherjee [aut],
Michael Kleinsasser [cre]

Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models.