Packaged: 2018-02-01 20:12:33 UTC; johnmuschelli

Imports: xml2 (>= 1.1.1), base64enc, R.utils, tools, utils
Packaged: 2020-11-11 03:45:54 UTC; johnmuschelli

Title: Take Shell Commands and Turn Them into R Functions
Description: Wrapping command line functions into R functions, which in turn run the command line functions.
Imports: namespace, processx, utils, readr, tibble

Title: Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with
Description: 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
person(given = "Jon", family = "Boss", role = c("aut"), email = "gutian@umich.edu"),

Description: 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.

Description: 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. For binary outcome, data can be sampled in prospective cohort studies or case-control studies.

Description: 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. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.

Title: Group Iterative Multiple Model Estimation
Description: Automated identification and estimation of group- and
individual-level relations in time series data from within a structural