walker: Bayesian Generalized Linear Models with Time-Varying Coefficients (original) (raw)
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 |
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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:
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