Package: tulpa 0.7.5

tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

A general-purpose engine for fitting Bayesian hierarchical models with spatial fields, temporal effects, spatially varying coefficients, and multiple inference backends. Scalable spatial structure includes Hilbert space approximate Gaussian processes (HSGP; Riutort-Mayol et al. 2023 <doi:10.1007/s11222-022-10167-2>), nearest-neighbor Gaussian processes (NNGP; Datta et al. 2016 <doi:10.1080/01621459.2015.1044091>), intrinsic conditional autoregressive models (ICAR; Besag, York, and Mollie 1991 <doi:10.1007/BF00116466>), the reparameterized Besag-York-Mollie model (BYM2; Riebler et al. 2016 <doi:10.1177/0962280216660421>), and stochastic partial differential equation fields (SPDE; Lindgren, Rue, and Lindstrom 2011 <doi:10.1111/j.1467-9868.2011.00777.x>). Temporal structure covers random walks, autoregressive processes, and Gaussian processes. Inference is tiered by correctness guarantee: exact Hamiltonian Monte Carlo with the No-U-Turn sampler, Laplace and nested Laplace approximations with hyperparameter integration (Rue, Martino, and Chopin 2009 <doi:10.1111/j.1467-9868.2008.00700.x>), and variational inference. Model-specific packages plug observation likelihoods into the engine through a templated C++ callback interface.

Authors:Gilles Colling [aut, cre, cph], Frances Y. Kuo [ctb, cph], Stephen Joe [ctb, cph], Arm Limited [cph], The Walnutpie Developers [cph]