Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) , Bürkner (2018) , and Carpenter et al. (2017) .To cite package "bayesian" in publications use
Purpose: Bayesian multilevel models are increasingly used to overcome the limitations of frequentist...
Bayesian Optimization for Anything: A high-level Bayesian optimization framework and model wrapping ...
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis ...
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is...
Added gather_pairs method for creating custom scatterplot matrices (and more!) Ordinal models in br...
This is the first full draft containing brms versions of all of Kruschke's JAGS and Stan models, exc...
The brms package implements Bayesian multilevel models in R using the probabilistic programming lang...
This is the first full draft in that all chapters are largely fleshed out in terms of content. In ad...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
With the arrival of the R packages \fontencoding {T1}\texttt {nlme} and \fontencoding {T1}\texttt {l...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Advances made in computer development along with the curiosity regarding the use of data in the worl...
This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian mode...
This article focuses on presenting the possibilities of Bayesian modelling (Finite Mixture Modelling...
Purpose: Bayesian multilevel models are increasingly used to overcome the limitations of frequentist...
Bayesian Optimization for Anything: A high-level Bayesian optimization framework and model wrapping ...
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis ...
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is...
Added gather_pairs method for creating custom scatterplot matrices (and more!) Ordinal models in br...
This is the first full draft containing brms versions of all of Kruschke's JAGS and Stan models, exc...
The brms package implements Bayesian multilevel models in R using the probabilistic programming lang...
This is the first full draft in that all chapters are largely fleshed out in terms of content. In ad...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
With the arrival of the R packages \fontencoding {T1}\texttt {nlme} and \fontencoding {T1}\texttt {l...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Advances made in computer development along with the curiosity regarding the use of data in the worl...
This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian mode...
This article focuses on presenting the possibilities of Bayesian modelling (Finite Mixture Modelling...
Purpose: Bayesian multilevel models are increasingly used to overcome the limitations of frequentist...
Bayesian Optimization for Anything: A high-level Bayesian optimization framework and model wrapping ...
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis ...
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