A1 Journal article (refereed)
Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R (2022)


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


JYU authors or editors


Publication details

All authors or editors: Helske, Jouni

Journal or series: SoftwareX

eISSN: 2352-7110

Publication year: 2022

Publication date: 24/02/2022

Volume: 18

Article number: 101016

Publisher: Elsevier BV

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.softx.2022.101016

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/80507

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2009.07063

Additional information: Original software publication.


Abstract

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.


Keywords: time series; regression analysis; linear models; Bayesian analysis; Markov chains; Monte Carlo methods; R (programming languages)

Free keywords: Bayesian inference; time-varying regression; R; Markov chain Monte Carlo


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Preliminary JUFO rating: 1


Last updated on 2022-17-06 at 11:05