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(This article was first published on <strong><a href="https://r-nimble.org/version-0-6-11-of-nimble-released"> R – NIMBLE</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>We’ve released the newest version of NIMBLE on CRAN and on our website.</p>
<p>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.</p>
<p>New features include:</p>
<ul>
<li>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);</li>
<li>additional resampling methods available with the auxiliary and bootstrap particle filters;</li>
<li>user-defined filtering algorithms can be used with NIMBLE’s particle MCMC samplers;</li>
<li>MCMC thinning intervals can be modified at MCMC run-time;</li>
<li>both runMCMC() and nimbleMCMC() now drop burn-in samples before thinning, making their behavior consistent with each other;</li>
<li>increased functionality for the ‘setSeed’ argument in nimbleMCMC() and runMCMC();</li>
<li>new functionality for specifying the order in which sampler functions are executed in an MCMC; and</li>
<li>invalid dynamic indexes now result in a warning and NaN values but do not cause execution to error out, allowing MCMC sampling to continue.</li>
</ul>
<p>Please see the NEWS file in the installed package for more details</p>
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