RBaM v1.1.1

New version of RBaM released on CRAN.

• New functionality: runModel(), to perform a single run of any model available in RBaM.
• New functionality: inference functions, to compute standard likelihoods, priors and posteriors.
• New functionality: Markov Chain Monte Carlo (MCMC) samplers, in particular an adaptive Metropolis sampler MCMC_AM() largely inspired by Haario et al. (2001), and a one-at-a-time Metropolis sampler MCMC_OAAT().
• New functionality: SPD_estimate() to estimate a stage-period-discharge (SPD) BaRatin model.
• New functionality: 1-D hydrodynamical model 'MAGE_ZQV' available in the list of RBaM models.
• Bug fix: function runExe(), used by main function BaM(), was not handling the user-requested workspace properly.
• Miscellaneous minor changes.

Full Changelog: v1.0.1...v1.1.1

RBaM v1.0.1

First version of RBaM released on CRAN. RBaM aims at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. It provides tools for controlling and implementing the various building blocks of Bayesian 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). Examples of applications include Mansanarez et al. (2019) , Le Coz et al. (2021), Perret et al. (2021), Darienzo et al. (2021) and Perret et al. (2023).