NIMBLE version 1.4.0 released, plus new nimbleQuad package for quadrature-based algorithms

[Chris Paciorek]

We're very pleased to announce the release of version 1.4.0 of nimble as well as the first release of our new nimbleQuad package. 

nimbleQuad includes improved versions of our existing Laplace and AGHQ approximations, plus a new INLA-like deterministic nested posterior approximation whose methodology borrows from both INLA and the extended latent Gaussian models approach of the `aghq` package in R. Feedback and suggestions are of course welcome!

Version 1.4.0 of nimble provides important new and improved functionality, plus some bug fixes and improved error trapping.  Version 1.4.0 has been available on CRAN since mid-December.

The new and improved functionality includes:

• Laplace and AGHQ approximation (and the new INLA-like deterministic nested posterior approximation) now live in the nimbleQuad package. 
• A new system for computing and storing "derived quantities" during MCMC execution, allowing users to record additional quantities of interest at every saved MCMC iteration (i.e., following the thinning interval, or some other user-chosen interval). Derived quantities provided by NIMBLE include means, variances, model log-densities, and predictive nodes. Users can also define their own derived quantities.
• Matrix exponential functionality via `expm` and `expAv`.
• The ability to provide multiple code chunks to `nimbleCode` for greater flexibility in composing models.
• Greatly improved efficiency and memory use of AD system and efficiency improvements to Laplace/AGHQ approximation.

In addition to the new and improved functionality above, other bug fixes, improved error trapping, and enhancements include:

• Removing some documentation references to "BUGS" when referring to models.
• Allowing users to turn off `model$checkBasics` via a new option.
• Better handling inconsistencies between `inits` and `dimensions`.
• Making minor improvements to the Pólya-gamma sampler.
• Generalizing the system of dynamically generating conjugate MCMC samplers, to allow for multivariate parameters of dependent distributions to have distinct sizes from the dependent node itself .
• Making MCEM append new samples when increasing sample size using the ascent-based method, rather than starting a new sample.

Please see the release notes for more details.

- Chris Paciorek for the NIMBLE Development Team