Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R

By Jouni Helske in Bayesian Inference R Package

February 13, 2022



The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as changes in tax policy which gradually increases their effect over time. The Markov chain Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian Monte Carlo provided by Stan software, using a state space representation of the model to marginalise over the regression coefficients for efficient low-dimensional sampling.

Posted on:
February 13, 2022
1 minute read, 86 words
Bayesian Inference R Package
Markov Chain Monte Carlo Time Series
See Also:
Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models
Spatio-temporal modeling of co-dynamics of smallpox, measles and pertussis in pre-healthcare Finland
dynamite: An R Package for Dynamic Multivariate Panel Models