v1.4.0

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 1.4.0 provides important new and improved functionality, plus some bug fixes and improved error trapping.

The new and improved functionality includes:

• 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. This new approximation and NIMBLE’s existing Laplace and AGHQ approximation now live in a new package, nimbleQuad, rather than in the core nimble 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 making 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.
• Various enhancements to error trapping and warning messages.
• A variety of minor bug fixes.

v1.3.0

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 1.3.0 provides some new and improved functionality, plus some bug fixes and improved error trapping.

The new and improved functionality includes:

• A new multivariate MCMC sampler, the Barker proposal sampler (sampler_barker). We encourage users to try this sampler in place of the block Metropolis RW_block sampler and let us know how well it works. The Barker sampler uses gradient information and may improve adaptation behavior, including better mixing when parameters are on different scales or the initial proposal scale is too large.
• An improved Laplace/AGHQ implementation that includes use of the nlminb optimizer for both inner and outer optimization (for better optimization performance), improved messaging and output naming, returning the log-likelihood and degrees of freedom for model selection calculations, and unified control of optimization method and other controls at either the build stage or through the updateSettings method.
• The addition of the BOBYQA optimization method through nimOptim, registered via nimOptimMethod.

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

• Preventing the use of nimbleFunction method names and nimbleFunction names that conflict with names in the nimble language (DSL).
• More carefully checking for and warning of cases of NaN and non-finite log probability values in various samplers that in some cases may indicate invalid MCMC sampling.
• More carefully handling of NaN and non-finite log probability values in the CRP sampler.
• Error trapping cases of dynamic indices producing a non-scalar result in AD-enabled models and provide a suggested work-around.
• Error trapping use of a non-existent nimbleList.
• Preventing use of a single seed when running multiple chains via runMCMC.
• Improving messaging related to lack of derivative support for functions.
• Adding information about model macros to the manual.
• Fixing bug in caching values in the CRP sampler when maximum number of clusters is exceeded, which would have caused incorrect sampling (albeit with the user having been warned that they should increase the maximum number of clusters).
• Fixing an issue preventing use of nimbleList elements in nimCat.
• Preventing an adaptation interval of one for various block samplers for which an interval of one leads to an error.
• Allowing runLaplace to use an uncompiled Laplace object.

v1.2.1

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

This is a micro release that primarily addresses some packaging changes requested by CRAN. In addition, this release includes:

• A multinomial MCMC sampler, sampler_RW_multinomial, for random variables following a multinomial distribution.
• Some enhancements to error trapping and warning messages.
• A variety of minor bug fixes.

v1.2.0

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 1.2.0 provides extensive new functionality, including:

• A Pólya-gamma sampler, sampler_polyagamma, for conjugate sampling of linear predictor parameters in logistic regression model specifications, including handling zero inflation and stochastic design matrices.
• A new sampler, sampler_noncentered, which samples the mean or standard deviation of a set of random effect values in a transformed space such that the random effects are deterministically shifted or scaled given new values of their hyperparameters. For random effects written in a centered parameterization, sampling is performed as if they had been written in a noncentered parameterization, thereby enabling a variant on the Yu and Meng (2011) interweaving sampling strategy of sampling in both parameterizations.
• A completely revamped MCEM algorithm, fixing a bug so that any parts of the model not connected to the latent states are included in MLE calculations, giving greater control and adding minor extensions to the ascent-based MCEM approach, using automatic derivatives in the maximization when possible, and converting buildMCEM to be a nimbleFunction rather than an R function.
• Adaptive Gauss-Hermite quadrature (AGHQ) for integrating over latent effects, as an extension of NIMBLE's Laplace approximation functionality. Also adds user-friendly R functions, runLaplace and runAGHQ, for using Laplace and AGHQ approximation for maximum likelihood estimation.
• A more flexible optimization system via nimOptim, with support for nlminb built in as well as the capability for users to provide potentially arbitrary optimization functions in R.
• Allowing the use of nimbleFunctions with setup code in models either for user-defined functions via <- or for user-defined distributions via ~. This supports holding large objects outside of model nodes for use in models.

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

• Erroring out if the RW_block sampler is assigned to any discrete nodes.
• Improving the speed of MCMC building in certain cases with many simple samplers by using inherits rather than is.
• Adding an argument to buildMCMC controlling whether to initialize values in the model.
• Improving the efficiency of setting up derivative information for models with multivariate nodes with many elements.
• Providing ability to control number of digits printed in C++ output.
• Allowing use of categorical MCMC sampler with user-specified dcat-like distributions.
• Warning of use of backward indexing in nimble models.
• Improve documentation of LKJ distribution and derivative tracking in the AD system.
• Fixing some internals related to memory handling in compiled code to avoid intermittent errors and crashes occurring in testing.
• Fixing a harmless typo causing partial name matching in R.
• Fixing an insufficient check for conjugacy in stickbreaking specifications.
• Removing spurious warning when returnType is character() in a nimbleFunction.
• Fixing incorrect error message when getParam used with non-existent node.
• Fixing compilation failures occurring on Red Hat Linux.
• Reenabling functionality for user-provided Eigen library and related updates to autoconf configuration.
• Enhancing functionality to support model macros.
• Removing deprecated is.na.vec and is.nan.vec.
• Removing deprecated dummy functions for compareMCMCs functions.

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.