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BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data
This article introduces the R package BayesCTDesign for two-arm randomized Bayesian
trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The…
trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The…
Bayesian Item Response Modeling in R with brms and Stan
Item response theory (IRT) is widely applied in the human sciences to model persons’
responses on a set of items measuring one or more latent constructs. While several
R packages have been developed that implement IRT models, they tend to be…
responses on a set of items measuring one or more latent constructs. While several
R packages have been developed that implement IRT models, they tend to be…
Bayesian Multivariate Spatial Models for Lattice Data with INLA
The INLAMSM package for the R programming language provides a collection of
multivariate spatial models for lattice data that can be used with the INLA package for
Bayesian inference. The multivariate spatial models implemented include different…
multivariate spatial models for lattice data that can be used with the INLA package for
Bayesian inference. The multivariate spatial models implemented include different…
Bayesian Random-Effects Meta-Analysis Using the bayesmeta R Package
The random-effects or normal-normal hierarchical model is commonly utilized in a
wide range of meta-analysis applications. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based only on few…
wide range of meta-analysis applications. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based only on few…
Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for
structure learning and sampling of Bayesian networks. The package includes tools to
search for a maximum a posteriori (MAP) graph and to sample graphs from the…
structure learning and sampling of Bayesian networks. The package includes tools to
search for a maximum a posteriori (MAP) graph and to sample graphs from the…
bayesnec: An R Package for Concentration-Response Modeling and Estimation of Toxicity Metrics
The bayesnec package has been developed for R to fit concentration (dose)-response
curves (CR) to toxicity data for the purpose of deriving no-effect-concentration (NEC), nosignificant-effect-concentration (NSEC), and effect-concentration (of…
curves (CR) to toxicity data for the purpose of deriving no-effect-concentration (NEC), nosignificant-effect-concentration (NSEC), and effect-concentration (of…
BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks
The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed
(discrete and continuous). Probabilistic reasoning enables a user to absorb…
(discrete and continuous). Probabilistic reasoning enables a user to absorb…
BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression
In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with
high-dimensional genomic and other omics data, a problem that can be studied…
high-dimensional genomic and other omics data, a problem that can be studied…
bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R
Jun Woo, Jinhua Wang
Non-regression-based inferences, such as discriminant analysis, can account for the
effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a…
effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a…
Behavioural observation tool for patient involvement and collaboration in emergency care teams (PIC-ET-tool)
Background Patient participation is advocated in various healthcare settings. Instruments for assessment and feedback have been developed to strengthen clinician-patient interaction. In an emergency department context, such
instruments are still…
instruments are still…