nimble - MCMC, Particle Filtering, and Programmable Hierarchical Modeling

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>.

Last updated 1 months ago

bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp

12.87 score 167 stars 18 dependents 2.6k scripts 3.6k downloads

nimbleEcology - Distributions for Ecological Models in 'nimble'

Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

Last updated 1 months ago

7.01 score 18 stars 94 scripts 368 downloads

compareMCMCs - Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the 'nimble' package or from other packages. Plug-ins for JAGS via 'rjags' and Stan via 'rstan' are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

Last updated 5 months ago

4.71 score 1 stars 17 scripts 263 downloads

nimbleSMC - Sequential Monte Carlo Methods for 'nimble'

Includes five particle filtering algorithms for use with state space models in the 'nimble' system: 'Auxiliary', 'Bootstrap', 'Ensemble Kalman filter', 'Iterated Filtering 2', and 'Liu-West', as described in Michaud et al. (2021), <doi:10.18637/jss.v100.i03>. A full User Manual is available at <https://r-nimble.org>.

Last updated 1 months ago

4.62 score 2 stars 35 scripts 567 downloads