R Package rethinking: Statistical Rethinking Course and Book. This R package accompanies a course and book on Bayesian data analysis (McElreath 2016. Statistical Rethinking. CRC Press.). It contains tools for conducting both MAP estimation and Hamiltonian Monte Carlo (through RStan - mc-stan.org). These tools force the user to specify the model as a list of explicit distributional assumptions. This is more tedious than typical formula-based tools, but it is also much more flexible and powerful.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Barraquand, Frédéric; Gimenez, Olivier: Fitting stochastic predator-prey models using both population density and kill rate data (2021)
- Kelter, Riko: Bayesian model selection in the (\mathcalM)-open setting -- approximate posterior inference and subsampling for efficient large-scale leave-one-out cross-validation via the difference estimator (2021)
- Rothkopf, Alexander: Heavy quarkonium in extreme conditions (2020)
- Steinbuch, Luc; Orton, Thomas G.; Brus, Dick J.: Model-based geostatistics from a Bayesian perspective: investigating area-to-point Kriging with small data sets (2020)
- Wang, Sunpeng; Pan, Yang; Wang, Quanyi; Miao, Hongyu; Brown, Ashley N.; Rong, Libin: Modeling the viral dynamics of SARS-CoV-2 infection (2020)
- Calvin, K., Link, R. and Wise, M.: gcamland v1.0 - An R Package for Modelling Land Use and Land Cover Change (2019) not zbMATH
- Merkle, Edgar C.; Furr, Daniel; Rabe-Hesketh, Sophia: Bayesian comparison of latent variable models: conditional versus marginal likelihoods (2019)
- Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH