Category Archives: Releases

Version 0.9.0 of NIMBLE released

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We’ve released the newest version of NIMBLE on CRAN and on our website. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC). Version 0.9.0 provides some new features as well as providing a variety of speed improvements, better output handling, and bug fixes.

New features and bug fixes include:

  • added an iterated filtering 2 (IF2) algorithm (a sequential Monte Carlo (SMC) method) for parameter estimation via maximum likelihood;
  • fixed several bugs in our SMC algorithms;
  • improved the speed of MCMC configuration;
  • improved the user interface for interacting with the MCMC configuration; and
  • improved our conjugacy checking system to detect some additional cases of conjugacy.

Please see the release notes on our website for more details.

 

Version 0.8.0 of NIMBLE released

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We’ve released the newest version of NIMBLE on CRAN and on our website. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC). Version 0.8.0 provides some new features, speed improvements, and a variety of bug fixes and better error/warning messages.

New features include:

  • a reversible jump MCMC sampler for variable selection via configureRJ();
  • greatly improved speed of MCMC sampling for Bayesian nonparametric models with a dCRP distribution by not sampling parameters for empty clusters;
  • experimental faster MCMC configuration, available by setting nimbleOptions(oldConjugacyChecking = FALSE) and nimbleOptions(useNewConfigureMCMC = TRUE);
  • and improved warning and error messages for MCEM and slice sampling.

Please see the release notes on our website for more details.

Version 0.7.1 of NIMBLE released

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We’ve released the newest version of NIMBLE on CRAN and on our website. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC).

Version 0.7.1 is primarily a maintenance release with a couple important bug fixes and a few additional features. Users with large models and users of the dCRP Bayesian nonparametric distribution are strongly encouraged to update to this version to pick up the bug fixes related to these uses.

New features include:

  • recognition of normal-normal conjugacy in additional multivariate regression settings;
  • handling of six-dimensional arrays in models.

Please see the release notes on our website for more details.

Version 0.7.0 of NIMBLE released

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We’ve released the newest version of NIMBLE on CRAN and on our website. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC). Version 0.7.0 provides a variety of new features, as well as various bug fixes.

New features include:

  • greatly improved efficiency of sampling for Bayesian nonparametric (BNP) mixture models that use the dCRP (Chinese Restaurant process) distribution;
  • addition of the double exponential (Laplace) distribution for use in models and nimbleFunctions;
  • a new “RW_wishart” MCMC sampler, for sampling non-conjugate Wishart and inverse-Wishart nodes;
  • handling of the normal-inverse gamma conjugacy for BNP mixture models using the dCRP distribution;
  • enhanced functionality of the getSamplesDPmeasure function for posterior sampling from BNP random measures with Dirichlet process priors.
  • handling of five-dimensional arrays in models;
  • enhanced warning messages; and
  • an HTML version of the NIMBLE manual.

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

Version 0.6-12 of NIMBLE released

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We’ve released the newest version of NIMBLE on CRAN and on our website. Version 0.6-12 is primarily a maintenance release with various bug fixes.

Changes include:

  • a fix for the bootstrap particle filter to correctly calculate weights when particles are not resampled (the filter had been omitting the previous weights when calculating the new weights);
  • addition of an option to print MCMC samplers of a particular type;
  • avoiding an overly-aggressive check for ragged arrays when building models; and
  • avoiding assigning a sampler to non-conjugacy inverse-Wishart nodes (thereby matching our handling of Wishart nodes).

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

Version 0.6-11 of NIMBLE released

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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

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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

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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

Version 0.6-8 of NIMBLE released

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We’ve released the newest version of NIMBLE on CRAN and on our website a week ago. Version 0.6-8 has a few new features, and more are on the way in the next few months.

New features include:

  • the proper Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_proper, which behaves similarly to BUGS’ car.proper distribution;
  • a new nimbleMCMC function that provides one-line invocation of NIMBLE’s MCMC engine, akin to usage of JAGS and WinBUGS through R;
  • a new runCrossValidate function that will conduct k-fold cross-validation of NIMBLE models fit by MCMC;
  • dynamic indexing in BUGS code is now allowed by default;
  • and a variety of bug fixes and efficiency improvements.

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

Version 0.6-6 of NIMBLE released!

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We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-6 has some important new features, and more are on the way in the next few months.

Эти артефакты не имели практической или коммерческой ценности. На самом деле их продажа была строго запрещена обычаем. А так как предметы всегда находились в движении, их владельцы редко носили их. Тем не менее, массимы совершали модные подарки подруге долгие путешествия, чтобы обменять их, рискуя жизнью и здоровьем, когда они путешествовали по коварным водам Тихого океана на своих шатких каноэ.

New features include:

  • dynamic indexes are now allowed in BUGS code — indexes of a variable no longer need to be constants but can be other nodes or functions of other nodes; for this release this is a beta feature that needs to be enabled with nimbleOptions(allowDynamicIndexing = TRUE);
  • the intrinsic Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_normal, which behaves similarly to BUGS’ car.normal distribution;
  • optim is now part of the NIMBLE language and can be used in nimbleFunctions;
  • it is possible to call out to external compiled code or back to R functions from a nimbleFunction using nimbleExternalCall() and nimbleRcall() (this is an experimental feature);
  • the WAIC model selection criterion can be calculated using the calculateWAIC() method for MCMC objects;
  • the bootstrap and auxiliary particle filters can now return their ESS values;
  • and a variety of bug fixes.

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

Finally, we’re deep in the midst of development work to enable automatic differentiation, Tensorflow as an alternative back-end computational engine, additional spatial models, and Bayesian nonparametrics.