–  In particular, it fixes a bug in retrieving parameter values from distributions that was introduced in version 0.10.0. The bug can cause incorrect behavior of conjugate MCMC samplers under certain model structures (such as particular state-space models), so we strongly encourage users to upgrade to 0.10.1.
– In addition, version 0.10.1 restricts use of WAIC to the conditional version of WAIC (conditioning on all parameters directly involved in the likelihood). Previous versions of nimble gave incorrect results when not conditioning on all parameters directly involved in the likelihood (i.e., when not monitoring all such parameters). In a future version of nimble we plan to make a number of improvements to WAIC, including allowing use of marginal versions of WAIC, where the WAIC calculation integrates over random effects.

We’ve moved NIMBLE’s various sequential Monte Carlo (SMC) algorithms (bootstrap particle filter, auxiliary particle filter, ensemble Kalman filter, iterated filter2, and particle MCMC algorithms) to the new nimbleSMC package. So if you want to use any of these methods as of nimble version 0.10.0, please make sure to install the nimbleSMC package. Any existing code you have that uses any SMC functionality should continue to work as is.

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).

• greatly extended NIMBLE’s Chinese Restaurant Process (CRP)-based Bayesian nonparametrics functionality by allowing multiple observations to be grouped together;
• fixed a bug giving incorrect results in our cross-validation function, runCrossValidate();
• moved NIMBLE’s sequential Monte Carlo (SMC, aka particle filtering) methods into the nimbleSMC package; and
• improved the efficiency of model and MCMC building and compilation.

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

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 0.9.1 is primarily a bug fix release but also provides some minor improvements in functionality.

Users of NIMBLE in R 4.0 on Windows MUST upgrade to this release for NIMBLE to work.

New features and bug fixes include:

• switched to use of system2() from system() to avoid an issue on Windows in R 4.0;
• modified various adaptive MCMC samplers so the exponent controlling the scale decay of the adaptation is adjustable by user;
• allowed pmin() and pmax() to be used in models;
• improved handling of NA values in the dCRP distribution; and
• improved handling of cases where indexing goes beyond the extent of a variable in expandNodeNames() and related queries of model structure.

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

Prepared by Ben Goldstein.

What is nimbleEcology?

nimbleEcology is an auxiliary nimble package for ecologists.

nimbleEcology contains a set of distributions corresponding to some common ecological models. When the package is loaded, these distributions are registered to NIMBLE and can be used directly in models.

nimbleEcology contains distributions often used in modeling abundance, occupancy and capture-recapture studies.

Why use nimbleEcology?

Ecological models for abundance, occupancy and capture-recapture often involve many discrete latent states. Writing such models can be error-prone and in some cases can lead to slow MCMC mixing. We’ve put together a collection of distributions in nimble to make writing these models easier

• Easy to use. Using a nimbleEcology distribution is easier than writing out probabilities or hierarchical model descriptions.
• Minimize errors. You don’t have to lose hours looking for the misplaced minus sign; the distributions are checked and tested.
• Integrate over latent states. nimbleEcology implementations integrate or sum likelihoods over latent states. This eliminates the need for sampling these latent variables, which in some cases can provide efficiency gains, and allows maximum likelihood (ML) estimation methods with hierarchical models.

How to use

nimbleEcology can be installed directly from CRAN as follows.

install.packages("nimbleEcology")

Once nimbleEcology is installed, load it using library. It will also load nimble.

library(nimbleEcology)

## Loading required package: nimble

## nimble version 0.10.0 is loaded.
## For more information on NIMBLE and a User Manual,
## please visit http://R-nimble.org.

## 
## Attaching package: 'nimble'

## The following object is masked from 'package:stats':
## 
##     simulate

## Loading nimbleEcology. 
## Registering the following user-defined functions: 
## dOcc, dDynOcc, dCJS, dHMM, dDHMM

Note the message indicating which distribution families have been loaded.

Which distributions are available?

The following distributions are available in nimbleEcology.

• dOcc (occupancy model)
• dDynOcc (dynamic occupancy model)
• dHMM (hidden Markov model)
• dDHMM (dynamic hidden Markov model)
• dCJS (Cormack-Jolly-Seber or mark-recapture model)
• dNmixture (N-mixture model)
• dYourNewDistribution Do you have a custom distribution that would fit the package? Are we missing a distribution you need? Let us know! We actively encourage contributions through GitHub or direct communication.

Example code

The following code illustrates a NIMBLE model definition for an occupancy model using nimbleEcology. The model is specified, built, and used to simulate some data according to the occupancy distribution.

library(nimbleEcology)

occ_code <- nimbleCode({
  psi ~ dunif(0, 1)
  p ~ dunif(0, 1)
  for (s in 1:nsite) {
    x[s, 1:nvisit] ~ dOcc_s(probOcc = psi, probDetect = p,
                            len = nvisit)
  }
})

occ_model <- nimbleModel(occ_code,
               constants = list(nsite = 10, nvisit = 5),
               inits = list(psi = 0.5, p = 0.5))

## defining model...

## building model...

## setting data and initial values...

## running calculate on model (any error reports that follow may simply reflect missing values in model variables) ... 
## checking model sizes and dimensions... This model is not fully initialized. This is not an error. To see which variables are not initialized, use model$initializeInfo(). For more information on model initialization, see help(modelInitialization).
## model building finished.

set.seed(94)
occ_model$simulate("x")
occ_model$x

##       [,1] [,2] [,3] [,4] [,5]
##  [1,]    0    0    0    0    0
##  [2,]    0    0    0    0    0
##  [3,]    0    0    0    0    0
##  [4,]    0    0    0    0    0
##  [5,]    1    1    1    0    1
##  [6,]    0    0    0    1    0
##  [7,]    0    0    0    0    0
##  [8,]    0    0    0    0    1
##  [9,]    1    1    1    0    0
## [10,]    0    1    0    0    0

How to learn more

Once the package is installed, you can check out the package vignette with vignette(“nimbleEcology”).

Documentation is available for each distribution family using the R syntax ?distribution, for example

?dHMM

For more detail on marginalization in these distributions, see the paper “One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE” (Ponisio et al. 2020).