Publications by Chris Paciorek

Version 1.2.0 of NIMBLE released

14.06.2024

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, Laplace approximation, and SMC). This release provides provides extensive new functionality, including...

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Version 1.1.0 of NIMBLE released

04.02.2024

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,Laplace approximation, and SMC). This release provides new functionality as well as various bug fixes a...

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nimbleHMC version 0.2.0 released, providing improved HMC performance

20.09.2023

nimbleHMC provides Hamiltonian Monte Carlo samplers for use with NIMBLE, in particular NUTS samplers. NIMBLE’s HMC samplers can be flexibly assigned to a subset of model parameters, allowing users to consider various sampling configurations. We’ve released version 0.2.0 of nimbleHMC, which includes a new default NUTS sampler inspired by Stan’...

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Version 1.0.1 of NIMBLE released, fixing a bug in version 1.0.0 affecting certain models

21.06.2023

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 1.0.1 follows shortly after 1.0.0 and fixes an issue and a bug introduced in version ...

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Version 1.0.0 of NIMBLE released, providing automatic differentiation, Laplace approximation, and HMC sampling

31.05.2023

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 1.0.0 provides substantial new functionality. This includes: A Laplace approximation...

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Version 0.13.0 of NIMBLE released

29.11.2022

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 0.13.0 provides new functionality (in particular improved handling of predictive n...

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NIMBLE virtual short course, January 4-6, 2023

08.09.2022

We’ll be holding a virtual training workshop on NIMBLE, January 4-6, 2023 from 8 am to 1 pm US Pacific (California) time each day. 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). Recently we added support for aut...

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Beta version of NIMBLE with automatic differentiation, including HMC sampling and Laplace approximation

15.07.2022

We’re excited to announce that NIMBLE now supports automatic differentiation (AD), also known as algorithmic differentiation, in a beta version available on our website. In this beta version, NIMBLE now provides: Hamiltonian Monte Carlo (HMC) sampling for an entire parameter vector or arbitrary subsets of the parameter vector (i.e., combined w...

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Version 0.6-8 of NIMBLE released!

24.11.2017

We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-8 has a few new features, and more are on the way in the next few months. New features include: the proper Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_proper, which behaves similarly to BUGS’ car.proper distributi...

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Version 0.6-8 of NIMBLE released

04.12.2017

We’ve released the newest version of NIMBLE on CRAN and on our website a week ago. Version 0.6-8 has a few new features, and more are on the way in the next few months. New features include: the proper Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_proper, which behaves similarly to BUGS’ car.proper dist...

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