Bayesian Generalized Linear Models with Time-Varying Coefficients R package walker version 1.0.10 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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>). Software 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 Jouni Helske <jouni.helske@iki.fi> Comprehensive R Archive Network (CRAN) Jouni Helske [aut, cre] (ORCID: <https://orcid.org/0000-0001-7130-793X>) GPL (>= 3) 2024-08-30 application/tgz https://CRAN.R-project.org/package=walker doi:10.32614/CRAN.package.walker