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nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Description
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at https://r-nimble.org.
Author(s)
Maintainer: Christopher Paciorek paciorek@stat.berkeley.edu
Authors:
Perry de Valpine
Daniel Turek
Nick Michaud
Cliff Anderson-Bergman
Fritz Obermeyer
Claudia Wehrhahn Cortes (Bayesian nonparametrics system)
Abel Rodríguez (Bayesian nonparametrics system)
Duncan Temple Lang (packaging configuration)
Wei Zhang (Laplace approximation)
Sally Paganin (reversible jump MCMC)
Joshua Hug (WAIC)
Paul van Dam-Bates (AGHQ approximation, Pólya-Gamma sampler, nimIntegrate)
Other contributors:
Jagadish Babu (code for the compilation system for an early version of NIMBLE) [contributor]
Lauren Ponisio (contributions to the cross-validation code) [contributor]
Peter Sujan (multivariate t distribution code) [contributor]
See Also
Useful links:
nimble documentation built on June 22, 2024, 9:49 a.m.
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| nimble-package | R Documentation |
nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Description
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at https://r-nimble.org.
Author(s)
Maintainer: Christopher Paciorek paciorek@stat.berkeley.edu
Authors:
Perry de Valpine
Daniel Turek
Nick Michaud
Cliff Anderson-Bergman
Fritz Obermeyer
Claudia Wehrhahn Cortes (Bayesian nonparametrics system)
Abel Rodríguez (Bayesian nonparametrics system)
Duncan Temple Lang (packaging configuration)
Wei Zhang (Laplace approximation)
Sally Paganin (reversible jump MCMC)
Joshua Hug (WAIC)
Paul van Dam-Bates (AGHQ approximation, Pólya-Gamma sampler, nimIntegrate)
Other contributors:
Jagadish Babu (code for the compilation system for an early version of NIMBLE) [contributor]
Lauren Ponisio (contributions to the cross-validation code) [contributor]
Peter Sujan (multivariate t distribution code) [contributor]
See Also
Useful links:
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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