BMTAR: Bayesian Approach for MTAR Models with Missing Data

Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) <doi:10.1080/03610926.2014.990758>.

Version: 0.1.1
Depends: R (≥ 3.6.0)
Imports: Brobdingnag, MASS, MCMCpack, expm, ks, mvtnorm, compiler, doParallel, parallel, ggplot2
Published: 2021-01-19
DOI: 10.32614/CRAN.package.BMTAR
Author: Valeria Bejarano Salcedo, Sergio Alejandro Calderon Villanueva Andrey Duvan Rincon Torres
Maintainer: Andrey Duvan Rincon Torres <adrincont at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
In views: MissingData, TimeSeries
CRAN checks: BMTAR results


Reference manual: BMTAR.pdf


Package source: BMTAR_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BMTAR_0.1.1.tgz, r-oldrel (arm64): BMTAR_0.1.1.tgz, r-release (x86_64): BMTAR_0.1.1.tgz, r-oldrel (x86_64): BMTAR_0.1.1.tgz
Old sources: BMTAR archive


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