walker: Bayesian Regression with Time-Varying Coefficients

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.

Version: 0.2.0
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: 2017-07-12
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

Downloads:

Reference manual: walker.pdf
Vignettes: Efficient Bayesian generalized linear models with time-varying coefficients
Package source: walker_0.2.0.tar.gz
Windows binaries: r-devel: walker_0.2.0.zip, r-release: walker_0.2.0.zip, r-oldrel: walker_0.2.0.zip
OS X El Capitan binaries: r-release: walker_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: walker_0.2.0.tgz
Old sources: walker archive

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