I am a co-founder and core developer of NIMBLE, a general computational statistics system (R package nimble). NIMBLE stands for Numerical Inference for statistical Models using Bayesian and Likelihood Estimation. See more at NIMBLE’s web site.

At the time of this writing, NIMBLE has been used in around 250 peer-reviewed papers and 40 dissertations, numbers that is sure to grow faster than this page is updated.

NIMBLE is most commonly used for hierarchical Bayesian modeling with Markov chain Monte Carlo (MCMC) algorithms. However it is extensible for both models and algorithms, and it supports other methods such as maximum likelihood algorithms (e.g. Monte Carlo Expectation Maximization) and sequential Monte Carlo (aka particle filtering). NIMBLE has been used in fields ranging from analysis of NBA (basketball) shooting patterns to spatial epidemiology to astronomy, and of course ecology.

NIMBLE is under active development, limited by the time available to the core development team. It is free, open-source software, and contributions from others are very much welcome.

NIMBLE has been supported by several grants from the US National Science Foundation.