walker: Bayesian Generalized Linear Models with Time-VaryingCoefficients
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 |
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