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Protocols

JAGS specifications

Information


Unique identifier OMICS_23800
Name JAGS
Alternative name Just Another Gibbs Sampler
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, Java
License GNU General Public License version 2.0, MIT License
Computer skills Advanced
Version 4.3.0
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Joachim Vandekerckhove
  • person_outline Dominik Wabersich

Publication for Just Another Gibbs Sampler

JAGS citations

 (116)
library_books

A modified chain binomial model to analyse the ongoing measles epidemic in Greece, July 2017 to February 2018

2018
PMCID: 5930726
PMID: 29717695
DOI: 10.2807/1560-7917.ES.2018.23.17.18-00165

[…] easles cases in future weeks, as well as the final epidemic size, by sampling from multiple possible epidemic trajectories. We used the R software environment [] to perform all calculations and JAGS (Just Another Gibbs Sampler) [] to fit the model using MCMC. To summarise the posterior distributions for each parameter we report posterior medians and 95% credible intervals (CrI). Full JAGS and R co […]

library_books

Potential niche expansion of the American mink invading a remote island free of native predatory mammals

2018
PLoS One
PMCID: 5884534
PMID: 29617392
DOI: 10.1371/journal.pone.0194745

[…] We implemented models using program JAGS [], through package R2jags in R programing language []. We used 3 chains of Markov chain Monte Carlo (MCMC) to find 100,000 posterior distribution of the parameters of interest after a 20,000 bur […]

library_books

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

2018
Front Hum Neurosci
PMCID: 5879117
PMID: 29632480
DOI: 10.3389/fnhum.2018.00106

[…] sily created, built and tested with both behavioral and EEG data using multiple types of Markov Chain Monte Carlo (MCMC) sampling techniques (see Table ). Although still being developed and improved, JAGS (Plummer, ), Stan (Carpenter et al., ), and PyMC3 (Salvatier et al., ) are all recommended tools for these steps. These programs allows users to build almost any cognitive hierarchical models the […]

library_books

Assessing Top Down and Bottom Up Contributions to Auditory Stream Segregation and Integration With Polyphonic Music

2018
Front Neurosci
PMCID: 5845899
PMID: 29563861
DOI: 10.3389/fnins.2018.00121

[…] m a standard normal distribution would extend from −1.96 to 1.96. When an effect's HDI range includes zero it indicates there is probably no effect of respective condition. Models were estimated with JAGS (Just Another Gibbs Sampler, version 3.3.0) via its Matlab integration MATJAGS (version 1.3.1) employing Gibbs sampling Markov Chain Monte Carlo (MCMC) simulations. JAGS models are defined by nod […]

library_books

AMModels: An R package for storing models, data, and metadata to facilitate adaptive management

2018
PLoS One
PMCID: 5830045
PMID: 29489825
DOI: 10.1371/journal.pone.0188966

[…] ion is updated to the posterior distribution in light of new information, y. More precisely, p(θ|y)=p(θ)p(y|θ)∫p(θ)p(y|θ)dθ.Normally a Bayesian analysis in R would be conducted via a package such as rjags [], in which the analytical inputs are stored in an object of class jags and the model output is stored in an object of class mcmc.list. Here, though, to illustrate the complete flexibility of AM […]

library_books

Assessing cognitive dysfunction in Parkinson's disease: An online tool to detect visuo‐perceptual deficits

2018
Mov Disord
PMCID: 5901022
PMID: 29473691
DOI: 10.1002/mds.27311

[…] l Bayesian signal detection model to obtain posterior distributions of group‐level sensitivity (d') and bias (c) parameters. We used Markov Chain Monte Carlo using Gibbs sampling implemented in JAGS (Just Another Gibbs Sampler) in R to draw samples from the posterior distributions. We used uninformative (high variance) prior distributions on the group‐level estimates of d′ and criterion when fitti […]


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JAGS institution(s)
Department of Cognitive Sciences, University of California, Irvine, CA, USA; Department of Psychology, University of Tubingen, Germany
JAGS funding source(s)
Supported by a grant from the National Science Foundation’s Measurement, Methods, and Statistics panel and a grant from German Academic Exchange Service (PROMOS).

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