NIMBLE package for hierarchical modeling (MCMC and more) faster and more flexible in version 0.6-1

NIMBLE version 0.6-1 has been released on CRAN and at  r-nimble.org.  

NIMBLE is a system that allows you to:

• Write general hierarchical statistical models in BUGS code and create a corresponding model object to use in R.
• Build Markov chain Monte Carlo (MCMC), particle filters, Monte Carlo Expectation Maximization (MCEM), or write generic algorithms that can be applied to any model.
• Compile models and algorithms via problem-specific generated C++ that NIMBLE interfaces to R for you.

Most people associate BUGS with MCMC, but NIMBLE is about much more than that.  It implements and extends the BUGS language as a flexible system for model declaration and lets you do what you want with the resulting models.  Some of the cool things you can do with NIMBLE include:

• Extend BUGS with functions and distributions you write in R as nimbleFunctions, which will be automatically turned into C++ and compiled into your model.
• Program with models written in BUGS code: get and set values of variables, control model calculations, simulate new values, use different data sets in the same model, and more.
• Write your own MCMC samplers as nimbleFunctions and use them in combination with NIMBLE’s samplers.
• Write functions that use MCMC as one step of a larger algorithm.
• Use standard particle filter methods or write your own.
• Combine particle filters with MCMC as Particle MCMC methods.
• Write other kinds of model-generic algorithms as nimbleFunctions.
• Compile a subset of R’s math syntax to C++ automatically, without writing any C++ yourself.

Some early versions of NIMBLE were not on CRAN because NIMBLE’s system for on-the-fly compilation via generating and compiling C++ from R required some extra work for CRAN packaging, but now it’s there.  Compared to earlier versions, the new version is faster and more flexible in a lot of ways.  Building and compiling models and algorithms could sometimes get bogged down for large models, so we streamlined those steps quite a lot.   We’ve generally increased the efficiency of C++ generated by the NIMBLE compiler.  We’ve added functionality to what can be compiled to C++ from nimbleFunctions.  And we’ve added a bunch of better error-trapping and informative messages, although there is still a good way to go on that.   Give us a holler on the nimble-users list if you run into questions.