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: |
1.0.4 |
| Depends: |
bayesplot, R (≥ 3.4.0), Rcpp (≥ 0.12.9), rstan (≥ 2.18.1) |
| Imports: |
coda, dplyr, Hmisc, ggplot2, KFAS, loo, methods, RcppParallel, 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), RcppParallel |
| Suggests: |
diagis, gridExtra, knitr (≥ 1.11), rmarkdown (≥ 0.8.1), testthat |
| Published: |
2022-03-03 |
| 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: |
C++14, GNU make |
| Citation: |
walker citation info |
| Materials: |
README |
| CRAN checks: |
walker results |
Documentation:
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