Title: Sign Restrictions, Bayesian, Vector Autoregression Models
Authors@R: c(person("Christian", "Danne", email = "dannec@tcd.ie", role = c("aut", "cre")))
Imports: HI, minqa, mvnfast, utils, stats, methods, grDevices, graphics
Description: Provides routines for identifying structural shocks in vector autoregressions (VARs) using sign restrictions.
Packaged: 2015-12-21 00:17:42 UTC; christian

Title: Tests for Error Autocorrelation and ARCH Errors in Vector
Description: Implements the Wild bootstrap tests for autocorrelation in vector autoregressive models of Ahlgren, N. & Catani, P.
(2016, <doi:10.1007/s00362-016-0744-0>) and the Combined LM test for ARCH in VAR models of Catani, P. & Ahlgren, N.

Title: Tests for Error Autocorrelation, ARCH Errors, and Cointegration
(2016, <doi:10.1007/s00362-016-0744-0>), the Combined LM test for ARCH in VAR models of Catani, P. & Ahlgren, N.
(2016, <doi:10.1016/j.ecosta.2016.10.006>), and Bootstrap determination of the co-integration rank (Cavaliere, G., Rahbek, A.,

Title: Variational Bayes Latent Position Cluster Model for networks.
Description: A package to fit and simulate latent position and cluster
models for statistical networks using a Variational Bayes approximation.
Depends: MASS, boot, mclust, network, sna, ergm

models for statistical networks using a Variational Bayes

Depends: MASS, boot, mclust, network, ergm, sna

Title: Variational Bayes Latent Position Cluster Model for Networks
Description: Fit and simulate latent position and cluster
models for network data, using a fast Variational Bayes approximation.

Description: Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.

Title: The VBMA4hmm (Variational Bayesian Markov Model for hidden
Description: This package allows one to deal with a 2-states HMM with a
fixed Gaussian distribution as emission for one state and a
mixture of Gaussian for the other state. Furthermore, by
computing optimal variational weights, it calculates an
averaged estimator of the posterior probabilities.