Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R
Helske, J. (2022). Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R. SoftwareX, 18, Article 101016. https://doi.org/10.1016/j.softx.2022.101016
© 2022 The Author(s). Published by Elsevier B.V.
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 marginalize over the regression coefficients for efficient low-dimensional sampling.
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Related funder(s)Academy of Finland
Funding program(s)Research costs of Academy Research Fellow, AoF; Research profiles, AoF; Academy Project, AoF
Additional information about fundingThis work has been supported by the Academy of Finland research grants 284513, 312605, 311877, and 331817.
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