Package: shrinkTVPVAR 0.1.1

shrinkTVPVAR: Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with shrinkage priors. Details on the algorithms used are provided in Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.

Authors:Peter Knaus [aut, cre]

shrinkTVPVAR_0.1.1.tar.gz
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shrinkTVPVAR_0.1.1.tgz(r-4.4-x86_64)shrinkTVPVAR_0.1.1.tgz(r-4.4-arm64)shrinkTVPVAR_0.1.1.tgz(r-4.3-x86_64)shrinkTVPVAR_0.1.1.tgz(r-4.3-arm64)
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shrinkTVPVAR_0.1.1.tgz(r-4.4-emscripten)shrinkTVPVAR_0.1.1.tgz(r-4.3-emscripten)
shrinkTVPVAR.pdf |shrinkTVPVAR.html✨
shrinkTVPVAR/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 2 scripts 128 downloads 7 exports 11 dependencies

Last updated 2 months agofrom:b2807bf92b. Checks:OK: 8 WARNING: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-win-x86_64WARNINGNov 16 2024
R-4.5-linux-x86_64OKNov 16 2024
R-4.4-win-x86_64OKNov 16 2024
R-4.4-mac-x86_64OKNov 16 2024
R-4.4-mac-aarch64OKNov 16 2024
R-4.3-win-x86_64OKNov 16 2024
R-4.3-mac-x86_64OKNov 16 2024
R-4.3-mac-aarch64OKNov 16 2024

Exports:density_plotterforecast_shrinkTVPVARgen_TVP_paramsshrinkTVPVARsimTVPVARstate_plotterTV_heatmap

Dependencies:codaGIGrvglatticeRColorBrewerRcppRcppArmadilloRcppGSLRcppProgressshrinkTVPstochvolzoo