Search Best ARIMA, GARCH, and MS-GARCH Model
c(person(given = "Rajeev Ranjan",
family = "Kumar",
role = c("aut", "cre"),
email = "rrk.uasd@gmail.com"),
person(given = "Girish Kumar",
family = "Jha",
role =  c("aut", "ths", "ctb")),
person(given = "Dwijesh C.",
family = "Mishra",
role =  "ctb"),
person(given = "Neeraj",
family = "Budhlakoti",
role = "ctb"))
Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.
Rajeev Ranjan Kumar [aut, cre],
Girish Kumar Jha [aut, ths, ctb],
Dwijesh C. Mishra [ctb],
Neeraj Budhlakoti [ctb]

c( person("Yoann"  , "Robin", email = "yoann.robin.k@gmail.com"  , role = c("aut", "cre")),
person("Mathieu", "Vrac" , email = "mathieu.vrac@lsce.ipsl.fr", role = c("cph")) )
Implementation of several recent multivariate bias correction
methods with a unified interface to facilitate their use. A
description and comparison between methods can be found in François
et al, (2020) <doi:10.5194/esd-11-537-2020>.
Yoann Robin [aut, cre],
Mathieu Vrac [cph]

Structural Bayesian Information Criterion for Graphical Models
c(person(given = "Quang",
family = "Nguyen",
role = c("cre", "aut"),
email = "Quang.P.Nguyen.GR@dartmouth.edu",
comment = c(ORCID = "0000-0002-2072-3279")),
person(given = "Jie",
family = "Zhou",
role = c("aut"),
email = "Jie.Zhou-2@dartmouth.edu"),
person(given = "Anne",
family = "Hoen",
role = c("aut"),
email = "Anne.G.Hoen@dartmouth.edu"),
person(given = "Jiang",
family = "Gui",
role = c("aut"),
email = "Jiang.Gui@dartmouth.edu"))
This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review).
In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework.
Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.
Quang Nguyen [cre, aut] (<https://orcid.org/0000-0002-2072-3279>),
Jie Zhou [aut],
Anne Hoen [aut],
Jiang Gui [aut]