Title: Search Best ARIMA, GARCH, and MS-GARCH Model
Description: 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>.

Authors@R: 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")) )
Description: 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

Title: Structural Bayesian Information Criterion for Graphical Models
Description: 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.
Author: Quang Nguyen [cre, aut] (<https://orcid.org/0000-0002-2072-3279>),

Title: Inference for a Generalised SBM with a Split Merge Sampler
Authors@R: person("Matthew", "Ludkin", email = "m.ludkin1@lancaster.ac.uk", role = c("aut", "cre", "cph"))
Description: Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

Title: Generate Stochastic Branching Networks
person("Finnbar", "Lee", , "lee.finnbar@gmail.com", role = c("aut", "cre"))
Description: Generate Stochastic Branching Networks ('SBNs'). Used to model the branching structure of rivers.