walker: Bayesian Generalized Linear Models with Time-Varying Coefficients

Bayesian generalized linear models with time-varying coefficients. 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, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).

Version: 0.5.0
Depends: bayesplot, R (≥ 3.4.0), Rcpp (≥ 0.12.9), rstan (≥ 2.18.1)
Imports: coda, dplyr, Hmisc, ggplot2, KFAS, methods, rlang, rstantools (≥ 2.0.0)
LinkingTo: StanHeaders (≥ 2.18.0), rstan (≥ 2.18.1), BH (≥ 1.66.0), Rcpp (≥ 0.12.9), RcppArmadillo, RcppEigen (≥ 0.3.3.3.0)
Suggests: diagis, gridExtra, knitr (≥ 1.11), rmarkdown (≥ 0.8.1), testthat
Published: 2020-10-19
Author: Jouni Helske ORCID iD [aut, cre]
Maintainer: Jouni Helske <jouni.helske at iki.fi>
BugReports: https://github.com/helske/walker/issues
License: GPL (≥ 3)
URL: https://github.com/helske/walker
NeedsCompilation: yes
SystemRequirements: C++14, GNU make
Citation: walker citation info
Materials: README
CRAN checks: walker results

Downloads:

Reference manual: walker.pdf
Vignettes: Efficient Bayesian generalized linear models with time-varying coefficients
Package source: walker_0.5.0.tar.gz
Windows binaries: r-devel: walker_0.4.1-3.zip, r-release: walker_0.4.1-3.zip, r-oldrel: walker_0.5.0.zip
macOS binaries: r-release: walker_0.4.1-3.tgz, r-oldrel: walker_0.5.0.tgz
Old sources: walker archive

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