Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range 
  of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic 
  Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic 
  volatility models for univariate time series. Prior specifications are flexible and explicitly encourage 
  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed 
  and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes 
  factor and leave-one-out cross-validation methods. References: Hyndman (2017)
    <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

A high-performance probabilistic programming library that aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU (Central Processing Unit), GPU (Graphics Processing Unit), or TPU (Tensor Processing Unit) using 'JAX' (Just-In-Time compiled Accelerated linear algebra) as the computational backend: Sosa (2026) <doi:10.64898/2026.01.19.700318>.

Easy application of Bayesian inference for functional responses via 'brms'. This package allows to fit various FR models for single- and multi-prey experiments by providing nonlinear prediction functions for 'brms'. It uses dynamical prediction models to correct for prey depletion. The 'brms' framework facilitates statistical modeling and enables users to conveniently incorporate covariates such as temperature gradients, experimental treatment variables, or random effects that account for grouping in experimental units. Default 'brms' functions make it easy to perform model checking, model comparison and hypothesis testing. Potential statistical issues with data from feeding trials, such as overdispersion, can be resolved by effortlessly switching between likelihood functions. This package, together with its tutorials, should provide students and researchers with a comprehensive and integrated statistical framework for easily testing their hypotheses on trophic interactions. References: Rosenbaum and Rall (2018) <doi:10.1111/2041-210X.13039>; Rosenbaum et al. (2024) <doi:10.1111/2041-210X.14372>.