The BUGS project: Evolution, critique and future directions

By David J. Lunn, David J. Spiegelhalter, Andrew Thomas, N. G. Best. Statistics in Medicine, 2009

2,026 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/11dc0c04a27ec8f864893086430329efa7448335

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Citations in the corpus

A Stochastic Variational Framework for Fitting and Diagnosing Generalized Linear Mixed Models (2012, methodology): “arge simulated data set in Section 6.4, where penalized quasilikelihood converges too slowly. The GLM t was used instead for initialization. In all examples, tting via MCMC was performed in OpenBUGS (Lunn et al., 2009) through R by using R2OpenBUGS as an interface. R2OpenBUGS was adapted by Neal Tho”
Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE (2015, methodology): “We chose to build upon the BUGS language because it has been widely adopted (Lunn et al., 2009).”
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors (2014, methodology): “For a general-purpose software system like R-INLA, OpenBUGS (Lunn et al., 2009), JAGS (Plummer, 2003), or STAN (Stan Development Team, 2014) this is infeasible.”
Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion) (2019, methodology): “Adapted from Gelman et al. (2013). packages for MCMC sampling such as Stan (Carpenter et al., 2017), JAGS (Plummer, 2003), WinBUGS (Lunn et al., 2000), OpenBUGS (Lunn et al., 2009), PyMC3 (Salvatier et al., 2016), and NIMBLE (de Valpine et al., 2017), which together are estimated to have hundreds of”
Common bottlenose dolphins (Tursiops truncatus) in California waters: Cranial differentiation of coastal and offshore ecotypes (2011, methodology): “The Bayesian analyses were conducted with the BRugs v.0.5–3 package in R (Lunn et al. 2009).”
Spatial and spatio-temporal models with R-INLA. (2013, methodology): “Markov Chain Monte Carlo (MCMC) methods (Brooks et al., 2011; Robert and Casella, 2004), are normally used for Bayesian computation, arguably thanks to the wide popularity of the BUGS software (Lunn et al., 2009, 2012).”
Fire severity and landscape context effects on arboreal marsupials (2013, positive): “The model was fitted using OpenBUGS (Lunn et al., 2009) called from R (R Development Core Team, 2011) using the R2OpenBUGS package (Sturtz et al.”
Fast processing of diel oxygen curves: Estimating stream metabolism with BASE (BAyesian Single‐station Estimation) (2015, methodology): “The model is called from a script in the statistical software R (R Development Core Team 2014), which invokes OpenBUGS (Lunn et al. 2009) to run the Markov Chain Monte Carlo (MCMC) iterations.”
The value of self-report measures as indicators of driving behaviors among young drivers (2016, methodology): “The model was estimated by using the Markov Chain Monte Carlo (MCMC) method under the full hierarchical Bayesian framework in the open-source software package Openbugs (55).”
Stan: A Probabilistic Programming Language (2017, background): “(Gelman et al. 2013, Section 5.1) deﬁne a hierarchical model of the incidence of tumors in rats in control groups across trials; a very similiar model is deﬁned for mortality rates in pediatric surgeries across hospitals in (Lunn et al. 2000, 2009, Examples, Volume 1).”
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “Furthermore, computer packages that make model building and analysis relatively simple and accessible to researchers without a programming background are starting to make an appearance (Lunn et al. 2009; Plummer 2003).”
Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis (2015, methodology): “Estimation uses standard Markov chain Monte Carlo software, and the package requires one of JAGS (Plummer, 2003), OpenBUGS (Lunn et al., 2009), or WinBUGS (Lunn et al.”
Bayesian analysis of measurement error models using integrated nested Laplace approximations (2015, methodology): “, 2000), OpenBugs (Lunn et al., 2009), JAGS (Plummer, 2003) or MCMC samplers in R, such as MCMCpack (Martin et al.”