c(person(given = "Samuel",
family = "Thomas",
role = c("cre", "aut"),
email = "samthoma@alumni.iu.edu"),
person(given = "Wanzhu",
family = "Tu",
role = "ctb",
email = "wtu1@iu.edu"),
person(given = "Trustees of Columbia University (R/rstanMethods.R)",
role = "cph"))

Bayesian Estimation of the GARCH(1,1) Model with Student's t Innovations
This package provides the bayesGARCH function which performs the
Bayesian estimation of the GARCH(1,1) model with Student's t innovations.

Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations

Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
This package provides the bayesGARCH function which
performs the Bayesian estimation of the GARCH(1,1) model with
Student's t innovations.

person("David", "Ardia", role = c("aut", "cre"), email = "david.ardia@fsa.ulaval.ca")
Provides the bayesGARCH function which performs the
Bayesian estimation of the GARCH(1,1) model with Student's t innovations.

person("David", "Ardia", role = c("aut", "cre", "cph"),
email = "david.ardia.ch@gmail.com", comment = c(ORCID = "0000-0003-2823-782X"))
Provides the bayesGARCH() function which performs the
Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
David Ardia [aut, cre, cph] (<https://orcid.org/0000-0003-2823-782X>)

person("David", "Ardia", role = c("aut", "cre"), email = "david.ardia.ch@gmail.com")
Provides the bayesGARCH() function which performs the
Bayesian estimation of the GARCH(1,1) model with Student's t innovations.

Functions to implement Generalized Direct Sampling
This package contains functions to help users implement
the Generalized Direct Sampling algorithm for Bayesian
hierarchical models (Braun and Damien, 2012).

This package contains functions to help users implement the Generalized Direct Sampling algorithm for Bayesian hierarchical models (Braun and Damien, 2013). GDS is useful for sampling from posterior distributions for which there is a large number of conditionally independent heterogeneous units.
sparseHessianFD(>= 0.1.1), sparseMVN(>= 0.1.0), mvtnorm,
trustOptim (>= 0.8.3), plyr (>= 1.8)