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

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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.

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