Announcing the new nimbleMacros package on CRAN, which provides more compact ways to specify linear model components or other model components in the nimble hierarchical modeling language. You can also write your own model macros.

New versions of nimble and nimbleHMC are available on CRAN. nimble now includes a Barker block sampler for MCMC, better implementations of Laplace approximation and adaptive Gauss-Hermite quadrature, and calling any user-provided optimization function.

🚨 The second article of my PhD is out 🥳 We integrated commute-time distance⚡ into dynamic occupancy models using @R_nimble to model carnivore recolonisation 🦦🐱. Curious why we're using hierarchical models for connectivity analyses? Check out the blog post!

We've updated nimbleEcology to use nimble's automatic differentiation features, allowing its occupancy, capture-recapture, HMM, and N-mixture models to work with HMC, Laplace approximation, and other AD algorithms. https://t.co/PKdadDZVeL @Ben_R_Goldstein

Minor update to nimbleHMC, with No-U-turn Hamiltonian Monte Carlo samplers for nimble models: https://t.co/LwIXp6AWio. Version 0.2.2 includes better diagnostic checking for AD (automatic differentiation) support in any parts of a model to be sampled by HMC.

nimble 1.2.0 is out! (details: https://t.co/Kbznhp9HCQ). Includes adaptive Gauss-Hermite quadrature, better Laplace approx, Pólya-gamma sampler, noncentered sampler, revamped MCEM, new ways to provide your own distributions and functions with internal data, and some speedups.

Currently struggling with MCMC simulation analysis where some data sets require centered RE parameterization, other noncentered. Nimble just added a new sampler that does both 🤯🤯 https://t.co/rJBbIXeErC

We spent such a great time talking about ecological modeling and Bayesian Inference with @R_nimble at @IREC_CSIC_UCLM #IBER24! 🗺️🦌📊🐬📉Thanks Pepe Jimenez for your talk and all attendees for coming, we hope to repeat it soon! Materials (🇪🇸) at https://t.co/kMbbfUSdZN #rstats

Running these analyses are bit complicated, and I rely a lot on the amazing @R_nimble package to build my models. But to make things more user-friendly I developed baorista, a dedicated R package that put the most complicated things in the backend https://t.co/xB8s9LNw84

Todo preparado para el taller sobre Inferencia Bayesiana en Ecología con R y @R_nimble! Tres días en el @IREC_CSIC_UCLM hablando sobre ecología y estadśitica 📊🦌📉🐰. Más info: https://t.co/kMbbfUSdZN #dIBER #rstat #bayes @ValentinLauret Cheatsheet by @SoniaIllanas 😊

So we developed an alternative Bayesian approach, using the amazing @R_nimble R package. We come up with two solutions, a parametric approach based on the classic 's-shape' curve discussed in the literature and a more flexible non-parametric method.

NIMBLE, HMMs, GAMs for movement data, Bayesian MCMCs .. do not miss the fab workshopsvat #ISEC_2024 IN Swansea this July! https://t.co/OGy2BRLVek

— ABSTRACT SUBMISSION OPEN — The #ISEC2024 conference will showcase what’s cool and exciting in #statisticalecology. Want to be part of it? 🤓☔️ You can now submit your abstract for talk or poster. Just follow the link on our website: https://t.co/Z8gaLQq8Vx

nimble version 1.1.0 is released on CRAN. Blog post: https://t.co/uLuFfDyG7t. Highlights: updates to automatic differentiation for more general Hamiltonian Monte Carlo and Laplace approximation. Addition of (1D) 'integrate' function.

10-14 June 2024 5-day workshop on "Modeling distribution, abundance, demography and population dynamics using R, JAGS and NIMBLE" at the Spanish Game Research Center (IREC) in Ciudad Real 🇪🇸 by M. Kéry M. Schaub @GGuillera @jj_lahoz M. Victoria Jiménez-Franco J. Jiménez 🤩

We've released an updated version of Hamiltonian Monte Carlo (our implementation of the NUTS sampler) for nimble in package nimbleHMC ( https://t.co/LwIXp6AWio). Among other things, it fixes an efficiency glitch in the initial release.

If you're at #JSM2023 and interested in hearing the latest about nimble for MCMC and beyond, I'll be talking about Hamiltonian Monte Carlo (NUTS) and Laplace approximation using nimble's new automatic differentiation features. Wed 9:05-9:20 CC-206B.

Once again amazed by the @R_nimble magic 🤩 I'm trying to fit dynamic ODE-based models to noisy data w/ Bayes and MCMC. Calling R functions within the code w/ nimbleRcall() is a game changer, as well as the possibility to change samplers. https://t.co/8LNqcxqfN3 #RStats

Excited to be running a workshop on Bayesian capture-recapture inference w hidden Markov models, #rstats & @R_nimble 🥳 Thanks for the invitation @vibass7 🥰 Website is up and running w slides, code & data https://t.co/X7MYs8ec5X

nimble 1.0.1 is released: https://t.co/vJzlA6c2Wo This follows right on the heels of the major 1.0.0 release, with bug fixes so please do update.