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RBaM
View on CRAN: Click
here
Download and install RBaM package within the R console
Install from CRAN:
install.packages("RBaM")
Install from Github:
library("remotes")
install_github("cran/RBaM") Install by package version:
library("remotes")
install_version("RBaM", "1.1.2") Attach the package and use:
library("RBaM")
Maintained by
Benjamin Renard
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-07-10
Latest Update: 2025-07-10
Description:
An interface to the 'BaM' (Bayesian Modeling) engine, a 'Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of 'BaM' inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) <https://hal.science/hal-02606929v1>. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.
How to cite:
Benjamin Renard (2025). RBaM: Bayesian Modeling: Estimate a Computer Model and Make Uncertain Predictions. R package version 1.1.2, https://cran.r-project.org/web/packages/RBaM. Accessed 24 Oct. 2026.
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