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.
| Version: | 0.7.5 |
| Depends: | R (≥ 4.1.0) |
| Imports: | Rcpp (≥ 1.0.12), Matrix, tulpaMesh (≥ 0.1.3), generics, stats, tools, utils, methods, graphics, grDevices |
| LinkingTo: | Rcpp, RcppEigen, Matrix |
| Suggests: | testthat (≥ 3.0.0), bayesplot, coda, ggplot2, sf, terra, knitr, rmarkdown, posterior (≥ 1.5.0), loo (≥ 2.7.0), rstantools, lme4, nlme, lmtest, numDeriv, spdep, MASS, betareg, glmmTMB, pscl, tweedie, fmesher, patchwork, stars, statmod, withr |
| Published: | 2026-10-19 |
| DOI: | 10.32614/CRAN.package.tulpa |
| Author: | Gilles Colling 
[aut, cre, cph],
Frances Y. Kuo [ctb, cph] (Sobol direction numbers in
src/sobol_direction_numbers.h, BSD-3-clause),
Stephen Joe [ctb, cph] (Sobol direction numbers in
src/sobol_direction_numbers.h, BSD-3-clause),
Arm Limited [cph] (exp and log routines in src/fastmath.h and
src/fastmath_data.cpp, MIT),
The Walnutpie Developers [cph] (WALNUTS transition in
inst/include/tulpa/walnuts.h, ported from walnutpie, MIT) |
| Maintainer: | Gilles Colling <gilles.colling051 at gmail.com> |
| BugReports: | https://github.com/gcol33/tulpa/issues |
| License: | MIT + file LICENSE |
| Copyright: | see file COPYRIGHTS |
| URL: | https://github.com/gcol33/tulpa, https://gillescolling.com/tulpa/ |
| NeedsCompilation: | yes |
| SystemRequirements: | C++17 |
| Language: | en-US |
| Citation: | tulpa citation info |
| Materials: | NEWS |
| CRAN checks: | tulpa results |
Documentation:
| Reference manual: | tulpa.html , tulpa.pdf |
| Vignettes: |
Inference modes: the three tiers and how to choose one (source, R code) Getting started with tulpa (source, R code) Spatial fields in tulpa (source, R code) |
Downloads:
| Package source: | tulpa_0.7.5.tar.gz |
| Windows binaries: | r-devel: tulpa_0.6.0.zip, r-release: tulpa_0.7.5.zip, r-oldrel: tulpa_0.7.5.zip |
| macOS binaries: | r-release (arm64): tulpa_0.7.5.tgz, r-oldrel (arm64): tulpa_0.7.5.tgz, r-release (x86_64): tulpa_0.7.5.tgz, r-oldrel (x86_64): tulpa_0.7.5.tgz |
| Old sources: | tulpa archive |
Linking:
Please use the canonical form https://CRAN.R-project.org/package=tulpa to link to this page.