curl, ggplot2, gtable, jsonlite, xts, zoo
keyring, openssl, shiny, covr, knitr, rmarkdown, testthat (>=
3.0.0)

curl, ggplot2, gtable, jsonlite, tibble, xts, zoo

curl, ggplot2, jsonlite, tibble, xts, zoo

c(person(given = "Charlie",
family = "Gao",
role = c("aut", "cre"),
email = "charlie.gao@shikokuchuo.net",
comment = c(ORCID = "0000-0002-0750-061X")),
person(given = "Hibiki AI Limited",
email = "info@hibiki-ai.com",
role = "cph"))
Charlie Gao [aut, cre] (<https://orcid.org/0000-0002-0750-061X>),
Hibiki AI Limited [cph]

curl, ggplot2, jsonlite, shiny, tibble, xts, zoo
keyring, openssl, covr, knitr, rmarkdown, testthat (>= 3.0.0)

ggplot2, jsonlite, nanonext (>= 0.5.5), shiny, xts, zoo
keyring, covr, knitr, rmarkdown, testthat (>= 3.0.0)

ggplot2, jsonlite, nanonext (>= 0.6.0), shiny, xts, zoo

Empirical Bayes Variable Selection via ICM/M Algorithm
c(person("Vitara", "Pungpapong", role = c("aut", "cre"),
email = "vitara@cbs.chula.ac.th"),
person("Min", "Zhang", role = "aut",
email="minzhang@stat.purdue.edu"),
person("Dabao", "Zhang", role = "aut",
email = "zhangdb@stat.purdue.edu"))
Vitara Pungpapong [aut, cre],
Min Zhang [aut],
Dabao Zhang [aut]
Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).

c(person("Vitara", "Pungpapong", role = c("aut", "cre"),
email = "vitara@cbs.chula.ac.th"),
person("Min", "Zhang", role = "ctb", email="minzhang@purdue.edu"),
person("Dabao", "Zhang", role = "ctb", email = "zhangdb@purdue.edu"))
Vitara Pungpapong [aut, cre],
Min Zhang [ctb],
Dabao Zhang [ctb]
Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.