walker: Bayesian Generalized Linear Models with Time-Varying Coefficients

Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) 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:	1.0.10
Depends:	bayesplot, R (≥ 3.4.0), rstan (≥ 2.26.0)
Imports:	coda, dplyr, Hmisc, ggplot2, KFAS, loo, methods, Rcpp (≥ 0.12.9), RcppParallel, rlang, rstantools (≥ 2.0.0)
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.9), RcppArmadillo, RcppEigen (≥ 0.3.3.3.0), RcppParallel, rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0)
Suggests:	diagis, gridExtra, knitr (≥ 1.11), rmarkdown (≥ 0.8.1), testthat
Published:	2024-08-30
DOI:	10.32614/CRAN.package.walker
Author:	Jouni Helske [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:	GNU make
Citation:	walker citation info
Materials:	README
CRAN checks:	walker results

Documentation:

Reference manual:	walker.pdf
Vignettes:	Efficient Bayesian generalized linear models with time-varying coefficients (source, R code)

Downloads:

Package source:	walker_1.0.10.tar.gz
Windows binaries:	r-devel: walker_1.0.10.zip, r-release: walker_1.0.10.zip, r-oldrel: walker_1.0.10.zip
macOS binaries:	r-release (arm64): walker_1.0.10.tgz, r-oldrel (arm64): walker_1.0.10.tgz, r-release (x86_64): walker_1.0.10.tgz, r-oldrel (x86_64): walker_1.0.10.tgz
Old sources:	walker archive

Linking:

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