v1.1.0

NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC, Laplace approximation, and SMC).

Version 1.1.0 provides new functionality and a variety of bug fixes.

• Enhancing our automatic differentiation (AD) system to enhance use of AD (and therefore HMC sampling) in models, including use of stochastic indexing and CAR models, as well as allowing distributions and functions (whether user-defined or built-in) that lack AD support (such as dinterval, dconstraint, and truncated distributions) to be used and compiled in AD-enabled models.
• Adding nimIntegrate to the NIMBLE language, providing one-dimensional numerical integration via adaptive quadrature, equivalent to R's integrate.
• Adding a "prior samples" MCMC sampler, which uses an existing set of numerical samples to define the prior distribution of model node(s).
• Better handling of CRP models under non-standard settings.
• Cleanly error trapping use of C++ keywords as model variable names.
• Removing the RW_multinomial MCMC sampler, which was found to generate incorrect posterior results.
• Fixing a bug in conjugacy checking in a case of subsets of multivariate nodes.
• Fixing is.na and is.nan to operate in the expected vectorized fashion.
• Improving documentation of AD, nimbleHMC, and nimbleSMC in the manual.
• Updating to Eigen version 3.4.0.

v1.0.1

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 a bug introduced in version 1.0.0 causing data to be set incorrectly in certain models and likely causing incorrect results from algorithms such as MCMC. The affected models are ones with autoregressive structure directly on data nodes and with a variable containing both data and elements appearing only on the right-hand side of model declarations (such as is often the case for capture-recapture models).

v1.0.0

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 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.

v0.13.2

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 0.13.2 exists solely to address some developer-level issues as required by CRAN and is identical to 0.13.1 from the user perspective.

v0.13.1

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 0.13.1 follows shortly after 0.13.0 and fixes a bug introduced in our new handling of predictive nodes in MCMC in version 0.13.0. The bug could affect MCMC results when using version 0.13.0 in models that both (1) have predictive nodes and (2) have multivariate nodes.

v0.13.0

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 a variety of new functionality and 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 arguments default and nodes 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.

v0.12.2

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 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 structure to rerun their analyses.

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.
• Fixing handling nimSeq default by value.
• Fixing access to member data more than two dimensions in a nested nimbleFunction.

v0.12.1

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 0.12.1 follows shortly after 0.12.0 and fixes a bug introduced in conjugacy processing in version 0.11.0 that causes incorrect MCMC sampling only in specific cases. The impacted cases have terms of the form "a[i] + x[i] * beta" (or more simply "x[i] * beta" or "a[i] + beta"), with beta subject to conjugate sampling and either (i) 'x' provided via NIMBLE's constants argument and x[1] == 1 or (ii) 'a' provided via NIMBLE's constants argument and a[1] == 0.

v0.12.0

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().

v0.11.1

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 0.11.1 is primarily a bug fix release that fixes a bug that was introduced in Version 0.11.0 (which was released on April 17, 2021) that affected MCMC sampling in MCMCs using the “posterior_predictive_branch” sampler introduced in version 0.11.0. This sampler would be listed by name when the MCMC configuration object is created and would be assigned to any set of multiple nodes that (as a group of nodes) have no data dependencies and are therefore sampled as a group from their predictive distributions.