Tag Archives: Releases

Version 0.6-11 of NIMBLE released

Posted on by

We’ve released the newest version of NIMBLE on CRAN and on our website.

Version 0.6-11 has important new features, notably support for Bayesian nonparametric mixture modeling, and more are on the way in the next few months.

New features include:

  • support for Bayesian nonparametric mixture modeling using Dirichlet process mixtures, with specialized MCMC samplers automatically assigned in NIMBLE’s default MCMC (See Chapter 10 of the manual for details);
  • additional resampling methods available with the auxiliary and bootstrap particle filters;
  • user-defined filtering algorithms can be used with NIMBLE’s particle MCMC samplers;
  • MCMC thinning intervals can be modified at MCMC run-time;
  • both runMCMC() and nimbleMCMC() now drop burn-in samples before thinning, making their behavior consistent with each other;
  • increased functionality for the ‘setSeed’ argument in nimbleMCMC() and runMCMC();
  • new functionality for specifying the order in which sampler functions are executed in an MCMC; and
  • invalid dynamic indexes now result in a warning and NaN values but do not cause execution to error out, allowing MCMC sampling to continue.

Please see the NEWS file in the installed package for more details

Version 0.6-10 of NIMBLE released

Posted on by

We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-10 primarily contains updates to the NIMBLE internals that may speed up building and compilation of models and algorithms, as well as a few bug fixes.

Changes include:

  • some steps of model and algorithm building and compilation are faster;
  • compiled execution with multivariate distributions or function arguments may be faster;
  • data can now be provided as a numeric data frame rather than a matrix;
  • to run WAIC, a user now must set ‘enableWAIC’ to TRUE, either in NIMBLE’s options or as an argument to buildMCMC();
  • if ‘enableWAIC’ is TRUE, buildMCMC() will now check to make sure that the nodes monitored by the MCMC algorithm will lead to a valid WAIC calculation; and
  • the use of identityMatrix() is deprecated in favor of diag().

Please see the NEWS file in the installed package for more details

Version 0.6-9 of NIMBLE released

Posted on by

We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-9 is primarily a maintenance release with various bug fixes and fixes for CRAN packaging issues.

New features include:

  • dimensions in a model will now be determined from either ‘inits’ or ‘data’ if not otherwise available;
  • one can now specify “nBootReps = NA” in the runCrossValidate() function, which will prevent the Monte Carlo error from being calculated;
  • runCrossValidate() now returns the averaged loss over all k folds, instead of the summed loss;
  • We’ve added the besselK function to the NIMBLE language;
  • and a variety of bug fixes.

Please see the NEWS file in the installed package for more details

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

Posted on by

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.

Version 0.4-1 released!

Posted on by

We’ve just released version 0.4-1, a minor release that fixes some logistical issues and adds a bit of functionality to our MCMC engine.

Changes as of Version 0.4-1 include:

  • added an elliptical slice sampler to the MCMC engine,
  • fixed bug preventing use of nimbleFunctions in packages depending on NIMBLE, and
  • reduced C++ compiler warnings on Windows during use of compileNimble.