nimbleHMC provides Hamiltonian Monte Carlo samplers for use with NIMBLE, in particular NUTS samplers. NIMBLE’s HMC samplers can be flexibly assigned to a subset of model parameters, allowing users to consider various sampling configurations.

We’ve released version 0.2.0 of nimbleHMC, which includes a new default NUTS sampler inspired by Stan’s implementation of NUTS. It also provides an updated version of our previous NUTS sampler (which is based on the original Hoffman and Gelman paper, and is now called the ‘NUTS_classic’ sampler in NIMBLE) that fixes performance issues in version 0.1.1.

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 1.0.0 provides substantial new functionality. This includes:

• A Laplace approximation algorithm that allows one to find the MLE for model parameters based on approximating the marginal likelihood in models with continuous random effects/latent process values.
• A Hamiltonian Monte Carlo (HMC) MCMC sampler implementing the NUTS algorithm (available in the newly-released nimbleHMC package).
• Support in NIMBLE’s algorithm programming system to obtain derivatives of functions and arbitrary calculations within models.
• A parameter transformation system allowing algorithms to work in unconstrained parameter spaces when model parameters have constrained domains.

These are documented via the R help system and a new section at the end of our User Manual. We’re excited for users to try out the new features and let us know of their experiences. In particular, given these major additions to the NIMBLE system, we anticipate the possibility of minor glitches. The best place to reach out for support is still the nimble-users list.

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

• Fixing a bug (previously reported in a nimble-users message) giving incorrect results in NIMBLE’s cross-validation function (`runCrossValidate`) for all but the ‘predictive’ loss function for NIMBLE versions 0.10.0 – 0.13.2.
• Fixing a bug in conjugacy checking causing incorrect identification of conjugate relationships in models with unusual uses of subsets, supersets, and slices of multivariate normal nodes.
• Improving control of the `addSampler` method for MCMC.
• Improving the WAIC system in a few small ways.
• Enhancing error trapping and warning messages.

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

We’re excited to announce that NIMBLE now supports automatic differentiation (AD), also known as algorithmic differentiation, in a beta version available on our website. In this beta version, NIMBLE now provides:

• Hamiltonian Monte Carlo (HMC) sampling for an entire parameter vector or arbitrary subsets of the parameter vector (i.e., combined with other samplers for the remaining parameters). 
• Laplace approximation for approximate integration over latent states in a model, allowing maximum likelihood estimation and MCMC based on the marginal likelihood (via the RW_llFunction samplers).
• The ability for users and algorithm developers to write nimbleFunctions that calculate derivatives of functions, including many but not all mathematical operations that are supported in the NIMBLE language.

We’re making this beta release available to allow our users to test and evaluate the AD functionality and the new algorithms, but it is not recommended for production use at this stage. So please give it a try, and let us know of any problems or suggestions you have, either via the nimble-users list, bug reports to our GitHub repository, or email to nimble.stats@gmail.com. 

You can download the beta version and view an extensive draft manual for the AD functionality.

We plan to release this functionality in the next NIMBLE release on CRAN in the coming months. 

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:

• Fixing an issue with reversible jump variable selection under a similar situation to the BNP issue discussed above (in particular where there are unknown hyperparameters of the regression coefficients being considered, which would likely be an unusual use case).
• Fixing a bug preventing setup of conjugate samplers for dwishart or dinvwishart nodes when using dynamic indexing.
• Fixing a bug preventing use of truncation bounds specified via `data` or `constants`.
• Fixing a bug preventing MCMC sampling with the LKJ prior for 2×2 matrices.
• Fixing a bug in `runCrossValidate` affecting extraction of multivariate nodes.
• Fixing a bug producing incorrect subset assignment into logical vectors in nimbleFunction code.
• Fixing a bug preventing use of `nimbleExternalCall` with a constant expression.
• Fixing a bug preventing use of recursion in nimbleFunctions without setup code.

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