BayesianTools
MCMC and SMC samplers and diagnostics for Bayesian statistics, with a focus on calibrating complex process-based models.
Cited 176 times on Google Scholar (as of 11 October 2026)
BayesianTools provides general-purpose samplers, plots and diagnostics for Bayesian statistics. It was written with a particular focus on calibrating complex process-based models, where every evaluation of the likelihood means running a simulation.
The model enters as an R function that returns the log-likelihood, so any simulation model that can be called from R can be calibrated, including those wrapped by rLPJGUESS and r3PG. Implemented samplers include several Metropolis variants (among them adaptive and delayed-rejection Metropolis–Hastings), the T-walk, two differential-evolution MCMCs, two DREAM samplers and a sequential Monte Carlo particle filter. Further functions cover convergence diagnostics, plotting and model comparison via the marginal likelihood and WAIC.
Authors: Florian Hartig (maintainer), Francesco Minunno, Stefan Paul. Contributors: David Cameron, Tankred Ott, Maximilian Pichler.
How to cite BayesianTools
Florian Hartig, Francesco Minunno, Stefan Paul. BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package.
doi:10.32614/CRAN.package.BayesianTools.
For the citation of a specific version, run citation("BayesianTools") in R.
Widely cited work citing BayesianTools
The most-cited articles that cite BayesianTools, ranked by their own citation count in OpenAlex; the group's own papers are left out.
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- Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory
- Warming may extend tree growing seasons and compensate for reduced carbon uptake during dry periods