Version 0.12.2 of NIMBLE released, including an important bug fix for some models using Bayesian nonparametrics with the dCRP distribution

release
announcement
Author

NIMBLE Development Team

Published

March 4, 2022

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 0.12.2 is a bug fix release. In particular, this release fixes a bug in our Bayesian nonparametric distribution (BNP) functionality that gives incorrect MCMC results for some models, specifically when using the dCRP distribution when the parameters of the mixture components (i.e., the clusters) have hyperparameters (i.e., the base measure parameters) that are unknown and sampled during the MCMC. Here is an example basic model structure that is affected by the bug:

k[1:n] ~ dCRP(alpha, n) for(i in 1:n) { y[i] ~ dnorm(mu[k[i]], 1) mu[i] ~ dnorm(mu0, 1) ## mixture component parameters with hyperparameter } mu0 ~ dnorm(0, 1) ## unknown cluster hyperparameter

(There is no problem without the hyperparameter layer - i.e., if mu0 is a fixed value – which is the situation in many models.)

We strongly encourage users using models with this type of structure to rerun their analyses, and we apologize for this issue.

Other changes in this release include:

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