walker (Q46775): Difference between revisions

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+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>).
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+Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R
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+Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo
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+Software:46775

Latest revision as of 01:08, 22 March 2024

Bayesian Generalized Linear Models with Time-Varying Coefficients

Language	Label	Description	Also known as
English	walker	Bayesian Generalized Linear Models with Time-Varying Coefficients

Statements

instance of

R package

0 references

software version identifier

1.0.6-1

publication date

16 October 2022

0 references

0.1.0

publication date

15 June 2017

0 references

0.2.0

publication date

12 July 2017

0 references

0.2.1

publication date

9 January 2018

0 references

0.2.2

publication date

16 October 2018

0 references

0.2.3-1

publication date

9 November 2018

0 references

0.2.3

publication date

23 October 2018

0 references

0.2.4-1

publication date

25 February 2019

0 references

0.2.4

publication date

15 February 2019

0 references

0.2.5

publication date

4 March 2019

0 references

0.3.0

publication date

22 September 2019

0 references

0.3.1-1

publication date

23 January 2020

0 references

0.4.0

publication date

15 May 2020

0 references

0.4.1-3

publication date

14 August 2020

0 references

0.5.0

publication date

19 October 2020

0 references

1.0.1-1

publication date

31 January 2021

0 references

1.0.1

publication date

25 January 2021

0 references

1.0.2

publication date

6 April 2021

0 references

1.0.3-1

publication date

31 January 2022

0 references

1.0.3

publication date

11 September 2021

0 references

1.0.4

publication date

3 March 2022

0 references

1.0.7

publication date

9 August 2023

0 references

1.0.8

publication date

11 September 2023

0 references

0 references

0 references

0 references

11 September 2023

0 references

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>).

0 references

author

Jouni Helske

0 references

copyright license

GNU General Public License

edition/version

≥ 3 (English)

0 references

depends on software

bayesplot

0 references