walker
type: Item
swMATH work ID: 35066
instance of: R package
software version identifier: 1.0.6-1
software version identifier: 0.1.0
software version identifier: 0.2.0
software version identifier: 0.2.1
software version identifier: 0.2.2
software version identifier: 0.2.3-1
software version identifier: 0.2.3
software version identifier: 0.2.4-1
software version identifier: 0.2.4
software version identifier: 0.2.5
software version identifier: 0.3.0
software version identifier: 0.3.1-1
software version identifier: 0.4.0
software version identifier: 0.4.1-3
software version identifier: 0.5.0
software version identifier: 1.0.1-1
software version identifier: 1.0.1
software version identifier: 1.0.2
software version identifier: 1.0.3-1
software version identifier: 1.0.3
software version identifier: 1.0.4
software version identifier: 1.0.7
software version identifier: 1.0.8
maintained by: Jouni Helske
CRAN project: walker
programmed in: R
MaRDI profile type: Software
last update: 2023-09-11T00:00:00Z
description: 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>).
author: Jouni Helske
copyright license: GNU General Public License
depends on software: bayesplot
depends on software: Rcpp
depends on software: rstan
depends on software: R
imports: coda
imports: dplyr
imports: Hmisc
imports: ggplot2
imports: KFAS
imports: loo
imports: methods
imports: RcppParallel
imports: rlang
imports: rstantools
cites work: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R
cites work: Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo
source code repository: walker
description: Bayesian Generalized Linear Models with Time-Varying Coefficients