Provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the 'nimble' modelling framework.  It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. 
  Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models.
  (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

Provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the 'nimble' package.  This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers.  The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>.  The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling. Functionality of the 'nimbleHMC' package is described further in Turek, et al (2024) <doi: 10.21105/joss.06745>.

Macros to generate 'nimble' code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the 'nimble' manual: <https://r-nimble.org/html_manual/cha-writing-models.html#subsec:macros>.

A collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.

Provides deterministic approximation methods for use with the 'nimble' package. These include Laplace approximation and higher-order extension of Laplace approximation using adaptive Gauss-Hermite quadrature (AGHQ), plus nested deterministic approximation methods related to the 'INLA' approach. Additional information is available in the NIMBLE User Manual and a 'nimbleQuad' tutorial, both available at <https://r-nimble.org/documentation.html>.