Skip to content

v0.12.0
Compare
Choose a tag to compare
@paciorek paciorek released this 01 Oct 23:19
· 1346 commits to devel since this release
v0.12.0
de1518f
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

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 0.12.0 provides a variety of new functionality, bug fixes, and improved error trapping, including:

  • completely revamping WAIC such that (1) by default WAIC is calculated in an online fashion without the need for any particular monitors, (2) either conditional or marginal (integrating over latent variables) WAIC can be calculated and data nodes can be grouped into joint likelihood terms, and (3) there is a new calculateWAIC() function that can compute (conditional) WAIC on a user-provided samples either in an MCMC object or a matrix;
  • adding the LKJ distribution, useful for prior distributions for correlation matrices, with default Metropolis-Hastings samplers executing on an unconstrained trasnformed parameter space;
  • fixing a bug in MCMC sampling of the dcar_proper distribution that results in incorrect MCMC results when the mean of the dcar_proper distribution is not the same for all elements of the node assigned the distribution;
  • fixing the isData() function to return TRUE whenever any elements of a multivariate data node are flagged as data;
  • correctly error trapping cases where a Bayesian nonparametric model has a differing number of dependent stochastic nodes (e.g., observations) or dependent deterministic nodes per group of elements clustered jointly, thereby preventing incorrect MCMC sampling in such cases, which were not previously detected; and
  • improving the formatting of standard logging messages produced by nimbleModel() and compileNimble().
Assets 2