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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=walker to link to this page.
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