Showing 2 of total 2 results (show query)
nimble-dev
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>.
Maintained by Christopher Paciorek. Last updated 18 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
169 stars 12.97 score 2.6k scripts 19 dependents
greta-dev
greta:Simple and Scalable Statistical Modelling in R
Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.
Maintained by Nicholas Tierney. Last updated 19 days ago.
566 stars 12.53 score 396 scripts 6 dependents