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.
- Version0.1.1
- R version≥ 3.6.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release01/19/2021
Documentation
Team
Andrey Duvan Rincon Torres
Valeria Bejarano Salcedo
Show author detailsRolesAuthorSergio Alejandro Calderon Villanueva
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Last 30 days
This package has been downloaded 249 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
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Last 365 days
This package has been downloaded 3,095 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jan 21, 2025 with 37 downloads.
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Dependencies
- Imports8 packages