nimbleHMC version 0.2.0 released, providing improved HMC performance

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.

Version 1.0.1 of NIMBLE released, fixing a bug in version 1.0.0 affecting certain models

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 1.0.1 follows shortly after 1.0.0 and fixes an issue and a bug introduced in version 1.0.0 causing data to be set incorrectly in certain models.
Both cases occur only when a variable (e.g., “x”) contains both stochastic nodes (e.g. “x[2] ~ <some distribution>”) and *either* deterministic nodes (e.g. “x[3] <- <some calculation>”) or right-hand-side-only nodes (e.g. “x[4]” appears only on the right-hand-side, like an explanatory value).
The issue involves a change of behavior (relative to previous nimble versions) when both setting data values for some nodes and initial values for other nodes within the same variable (that satisfies the previous condition). Data values for right-hand-side-only nodes were replaced by initial values (inits) if both were provided. Version 1.0.1 reverts to previous behavior that data values are not replaced by initial values in that situation.
The bug involves models where (for a variable satisfying the previous condition) not every scalar element within the variable is used as a node and some of the nodes in the variable are data. In that situation, data values may be set incorrectly. This could typically occur in models with autoregressive structure directly on some data nodes (such as may be the case for capture-recapture models involving many individual capture histories within the same variable, indexed by individual and time, with some individuals not present for the entire time series, resulting in unused scalar elements of the variable).
Please see the release notes on our website for more details.

Version 1.0.0 of NIMBLE released, providing automatic differentiation, Laplace approximation, and HMC sampling

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.

Version 0.13.0 of NIMBLE released

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 0.13.0 provides new functionality (in particular improved handling of predictive nodes in MCMC) and minor bug fixes, including:

  • Thoroughly revamping handling of posterior predictive nodes in the MCMC system, in particular that MCMC samplers, by default, will now exclude predictive dependencies from internal sampler calculations. This should improve MCMC mixing for models with predictive nodes. Posterior predictive nodes are now sampled conditional on all other model nodes at the end of each MCMC iteration.
  • Adding functionality to the MCMC configuration system, including a new replaceSamplers method and updates to the arguments for the addSamplers method.
  • Adding an option to the WAIC system to allow additional burnin (in addition to standard MCMC burnin) before calculating online WAIC, thereby allowing inspection of initial samples without forcing them to be used for WAIC.
  • Warning users of unused constants during model building.
  • Fixing bugs that prevented use of variables starting with ‘logProb’ or named ‘i’ in model code.
  • Fixing a bug to prevent infinite recursion in particular cases in conjugacy checking.
  • Fixing a bug in simulating from dcar_normal nodes when multiple nodes passed to simulate.
Please see the release notes on our website for more details.

NIMBLE virtual short course, January 4-6, 2023

We’ll be holding a virtual training workshop on NIMBLE, January 4-6, 2023 from 8 am to 1 pm US Pacific (California) time each day. 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).

Recently we added support for automatic differentiation (AD) to NIMBLE in a beta release, and the workshop will cover NIMBLE’s AD capabilities in detail.

The workshop will cover the following material:

  • the basic concepts and workflows for using NIMBLE and converting BUGS or JAGS models to work in NIMBLE.
  • overview of different MCMC sampling strategies and how to use them in NIMBLE, including Hamiltonian Monte Carlo (HMC).
  • writing new distributions and functions for more flexible modeling and more efficient computation.
  • tips and tricks for improving computational efficiency.
  • using advanced model components, including Bayesian non-parametric distributions (based on Dirichlet process priors), conditional auto-regressive (CAR) models for spatially correlated random fields, Laplace approximation, and reversible jump samplers for variable selection.
  • an introduction to programming new algorithms in NIMBLE.
  • use of automatic differentiation (AD) in algorithms.
  • calling R and compiled C++ code from compiled NIMBLE models or functions.

If you are interested in attending, please pre-register. Registration fees will be $125 (regular) or $50 (student).  We are also offering a process (see the pre-registration form) for students to request a fee waiver.

The workshop will assume attendees have a basic understanding of hierarchical/Bayesian models and MCMC, the BUGS (or JAGS) model language, and some familiarity with R.

Beta version of NIMBLE with automatic differentiation, including HMC sampling and Laplace approximation

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

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. 

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

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.


NIMBLE in-person short course, June 1-3, Lisbon, Portugal

We’ll be holding a in-person training workshop on NIMBLE, June 1-3, 2022, in Lisbon, Portugal, sponsored by the Centro de Estatística e Aplicações at the Universidade Lisboa (CEAUL).

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).

More details and registration are available at the workshop website. No previous NIMBLE experience is required, but the workshop will assume some familiarity with hierarchical models, Markov chain Monte Carlo (MCMC), and R.


NIMBLE online tutorial, November 18, 2021

We’ll be giving a two-hour tutorial on NIMBLE, sponsored by the environmental Bayes (enviBayes) section of ISBA (The International Society for Bayesian Analysis), on Thursday November 18, from 11 am to 1 pm US Eastern time.

NIMBLE ( is a system for fitting and programming with hierarchical models in R that builds on (a new implementation of) the BUGS language for declaring models. NIMBLE provides analysts with a flexible system for using MCMC, sequential Monte Carlo, MCEM, and other techniques on user-specified models. It provides developers and methodologists with the ability to write algorithms in an R-like syntax that can be easily disseminated to users. C++ versions of models and algorithms are created for speed, but these are manipulated from R without any need for analysts or algorithm developers to program in C++. While analysts can use NIMBLE as a nearly drop-in replacement for WinBUGS or JAGS, NIMBLE provides enhanced functionality in a number of ways.

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This workshop will demonstrate how one can use NIMBLE to:

  • flexibly specify an MCMC for a specific model, including choosing samplers and blocking approaches (and noting the potential usefulness of this for teaching);
  • tailor an MCMC to a specific model using user-defined distributions, user-defined functions, and vectorization;
  • write your own MCMC sampling algorithms and use them in combination with samplers from NIMBLE’s library of samplers;
  • develop and disseminate your own algorithms, building upon NIMBLE’s existing algorithms; and
  • use specialized model components such as Dirichlet processes, conditional auto-regressive (CAR) models, and reversible jump for variable selection.

The tutorial will assume working knowledge of hierarchical models and some familiarity with MCMC. Given the two-hour time frame, we’ll focus on demonstrating some of the key features of NIMBLE, without going into a lot of detail on any given topic.

To attend, please register here.