Package: BMTAR 0.1.1

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>.

Authors:Valeria Bejarano Salcedo <[email protected]>, Sergio Alejandro Calderon Villanueva <[email protected]> Andrey Duvan Rincon Torres <[email protected]>

BMTAR_0.1.1.tar.gz
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BMTAR.pdf |BMTAR.html
BMTAR/json (API)
NEWS

# Install 'BMTAR' in R:
install.packages('BMTAR', repos = c('https://adrincont.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/adrincont/bmtar/issues

Datasets:
  • datasim - Simulated Multivariate threshold autoregressive process simulation
  • datasim_miss - Multivariate threshold autoregressive process simulation with missing data
  • hydrodata - Hydrological data of Colombia
  • missingest - Simulated data

On CRAN:

2.70 score 1 stars 2 scripts 262 downloads 29 exports 51 dependencies

Last updated 3 years agofrom:06a5ec8913. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 16 2024
R-4.5-winERROROct 16 2024
R-4.5-linuxERROROct 16 2024
R-4.4-winERROROct 16 2024
R-4.4-macERROROct 16 2024
R-4.3-winERROROct 16 2024
R-4.3-macERROROct 16 2024

Exports:auto_mtarautoplotautoplot.regime_forecastautoplot.regime_missingautoplot.regime_modelautoplot.regime_numberautoplot.tsregimediagnostic_mtardmnormBdwishartBlists_indmtaregimemtarforecastmtarforecast.regime_modelmtariniparsmtarmissingmtarNAICmtarnsmtarnumregmtarsimmtarstrprint.regime_forecastprint.regime_missingprint.regime_modelprint.regime_numberprint.tsregimeprodBrepMtsregime

Dependencies:BrobdingnagclicodacodetoolscolorspacedoParallelexpmfansifarverFNNforeachggplot2gluegtableisobanditeratorskernlabKernSmoothkslabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmclustmcmcMCMCpackmgcvmulticoolmunsellmvtnormnlmepbapplypillarpkgconfigpracmaquantregR6RColorBrewerRcpprlangscalesSparseMsurvivaltibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Estimation of a MTAR model for some dataauto_mtar
Create a complete ggplot appropriate to a particular data typeautoplot
regime_forecast object ggplot for the outputs on the function outputs mtarforecastautoplot.regime_forecast
regime_missing object ggplot for the outputs on the function outputs mtarmissingautoplot.regime_missing
regime_model object ggplot for the outputs on the function outputs mtarns and mtastrautoplot.regime_model
regime_number object ggplot for the outputs on the function outputs mtarnumregautoplot.regime_number
tsregime object ggplot for the outputs on the function tsregimeautoplot.tsregime
Simulated Multivariate threshold autoregressive process simulationdatasim
Multivariate threshold autoregressive process simulation with missing datadatasim_miss
Multivariate threshold autoregressive process simulation for estimate number of regimesdatasim_numreg
Residual diagnosis for model MTARdiagnostic_mtar
Multivariate normal density using Brobdingnag classdmnormB
Wishart density using Brobdingnag classdwishartB
Hydrological data of Colombiahydrodata
Create indicator vector for the regimen of each observationlists_ind
simulated datamissingest
Object class "'regime'" creationmtaregime
Forecast for MTAR modelmtarforecast
Organization and check model specificationmtarinipars
Estimation of missing values of observed, covariate and threshold processesmtarmissing
Compute NAIC of a MTAR modelmtarNAIC
Estimation of non-structural parameters for MTAR modelmtarns
Estimation of the number of regimes in a MTAR modelmtarnumreg
Multivariate threshold autoregressive process simulationmtarsim
Estimation of structural parameters of MTAR modelmtarstr
print an object appropriate to a particular data typeprint
print regime_forecast object for the function outputs mtarforecastprint.regime_forecast
Print estimates of a regime_missing object of the function output mtarmissingprint.regime_missing
print regime_model object for the function outputs mtarns and mtastrprint.regime_model
print regime_number object for the function outputs mtarnumregprint.regime_number
Print tsregime objectprint.tsregime
Function to make product of elements of a listprodB
Function to create list of matrix objectsrepM
Creation of class "'tsregime'" for some datatsregime