Bayesian dynamic regression models where the regression coefficients can vary over time as random walks. Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, walker uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2017, <arXiv:1609.02541>).
| Version: | 0.2.1 |
| Depends: | R (≥ 3.0.2), Rcpp (≥ 0.12.9), bayesplot, rstan (≥ 2.16.2) |
| Imports: | dplyr, ggplot2, KFAS, methods |
| LinkingTo: | StanHeaders (≥ 2.16.0), rstan (≥ 2.16.2), BH (≥ 1.62.0.1), Rcpp (≥ 0.12.9), RcppArmadillo, RcppEigen (≥ 0.3.3.0) |
| Suggests: | diagis, gridExtra, knitr (≥ 1.11), rmarkdown (≥ 0.8.1), testthat |
| Published: | 2018-01-09 |
| Author: | Jouni Helske |
| Maintainer: | Jouni Helske <jouni.helske at iki.fi> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | walker citation info |
| CRAN checks: | walker results |
| Reference manual: | walker.pdf |
| Vignettes: |
Efficient Bayesian generalized linear models with time-varying coefficients |
| Package source: | walker_0.2.1.tar.gz |
| Windows binaries: | r-devel: walker_0.2.1.zip, r-release: walker_0.2.1.zip, r-oldrel: walker_0.2.1.zip |
| OS X El Capitan binaries: | r-release: walker_0.1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: walker_0.2.1.tgz |
| Old sources: | walker archive |
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