Programming with models: writing statistical algorithms for general model structures with NIMBLE

TL;DR

NIMBLE is a system within R that enables flexible specification of statistical models and efficient algorithm programming through a domain-specific language, supporting various inference methods like MCMC and importance sampling.

Contribution

The paper introduces NIMBLE, a novel system that extends BUGS for model specification and provides a flexible, efficient language for programming statistical algorithms within R.

Findings

Supports flexible model specification via extended BUGS language

Enables efficient algorithm execution through C++ compilation and linear algebra optimization

Demonstrates applications in MCMC, importance sampling, and MCEM

Abstract

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This paper provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM).