Visually Learning the Graphical Structure of Bayesian Networks
and Performing MCMC with 'Stan'
c(person("Viet-Phuong", "La", role = c("aut", "cre"), email = "lvphuong@gmail.com"), person("Quan-Hoang", "Vuong", role = c("aut"), email = "hoang.vuongquan@phenikaa-uni.edu.vn"))
Viet-Phuong La [aut, cre],
Quan-Hoang Vuong [aut]
coda, bnlearn, ggplot2, bayesplot, viridis, reshape2, dplyr
R (>= 3.4.0), rstan (>= 2.10.0), StanHeaders (>= 2.18.0),
stats, graphics, methods
Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The 'bayesvl' R package. Open Science Framework (May 18).

Global Bayesian Foraminifera Core Top Calibration
person("Steven", "Malevich", email = "malevich@email.arizona.edu", role = c("aut", "cre"))
A Bayesian, global planktic foraminifera core top calibration to
modern sea-surface temperatures. Includes four calibration models,
considering species-specific calibration parameters and seasonality.

Computation of Bayes Factors for Common Biomedical Designs
c(
person(given = "Maximilian",
family = "Linde",
role = c("aut", "cre"),
email = "maximilian.linde.92@gmail.com",
comment = c(ORCID = "0000-0001-8421-090X")),
person(given = "Don",
family = "van Ravenzwaaij",
role= "aut",
email = "d.van.ravenzwaaij@rug.nl",
comment = c(ORCID = "0000-0002-5030-4091")),
person(given = "Quentin F.",
family = "Gronau",
role = "ctb",
email = "q.f.gronau@uva.nl",
comment = c(ORCID = "0000-0001-5510-6943")))
BAYesian inference for MEDical designs in R. Convenience functions
for the computation of Bayes factors for common biomedical research
designs. Implemented are functions to test the equivalence (equiv_bf),
non-inferiority (infer_bf), and superiority (super_bf) of an experimental
group compared to a control group. Bayes factors for these three tests can
be computed based on raw data (x, y) or summary statistics (n_x, n_y,
mean_x, mean_y, sd_x, sd_y [or ci_margin and ci_level]), making it possible
to reanalyse findings (e.g., from publications) without the need to obtain
the raw data.
Maximilian Linde [aut, cre] (<https://orcid.org/0000-0001-8421-090X>),
Don van Ravenzwaaij [aut] (<https://orcid.org/0000-0002-5030-4091>),
Quentin F. Gronau [ctb] (<https://orcid.org/0000-0001-5510-6943>)