Author: L. E. Nieto-Barajas, J. A. Garcia Bueno, E.A. Morones Ishikawa and
Description: Computes the hazard rate estimate as described by Nieto-Barajas &
Walker (2002), Nieto-Barajas (2003), Nieto-Barajas & Walker (2007) and
readr, survival, rlang, stringr, tidyr, progress, Brobdingnag
Packaged: 2021-01-16 23:37:50 UTC; emiliomorones

Title: Block Gibbs Sampler with Incomplete Multinomial Distribution
Author: Kwang Woo Ahn <kwooahn@mcw.edu>, Kung-Sik Chan
Maintainer: Kwang Woo Ahn <kwooahn@mcw.edu>
Description: Implement an efficient block Gibbs sampler with incomplete
data from a multinomial distribution taking values from the k
categories 1,2,...,k, where data are assumed to miss at random
and each missing datum belongs to one and only one of m
distinct non-empty proper subsets A1, A2,..., Am of 1,2,...,k
and the k categories are labelled such that only consecutive

Title: Bayesian Global Vector Autoregressions
Author: Maximilian Boeck [aut, cre] (<https://orcid.org/0000-0001-6024-8305>),
Description: Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available.
Imports: abind, bayesm, coda, doParallel, foreach, GIGrvg, graphics,
knitr, MASS, Matrix, methods, parallel, Rcpp (>= 1.0.3), stats,
Suggests: testthat (>= 2.1.0), rmarkdown
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, stochvol, GIGrvg