A package that handles discrete Bayesian network models and provides inference using the frequentist approach

Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria.  Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented.

c(person("Nikolay","Balov",role=c("aut","cre"), email="nhbalov@gmail.com", comment = "Balov (2013) <doi:10.1214/13-EJS802>"), person("Peter","Salzman",role=c("aut")))
Nikolay Balov [aut, cre] (Balov (2013) <doi:10.1214/13-EJS802>),
Peter Salzman [aut]

c(person("Elias Laurin", "Meyer", email = "elias.meyer@meduniwien.ac.at", role = c("aut","cre")),
person("Peter", "Mesenbrink", role = "ctb"),
person("Franz", "Koenig", role = "ctb"))
Elias Laurin Meyer [aut, cre],
Peter Mesenbrink [ctb],
Franz Koenig [ctb]
Cohort plAtform Trial Simulation whereby every cohort consists of two arms,
control and experimental treatment. Endpoints are co-primary binary endpoints and
decisions are made using either Bayesian or frequentist decision rules.
Realistic trial trajectories are simulated and the operating characteristics of the
designs are calculated.
dplyr, purrr, ggplot2, plotly, tidyr, parallel, doParallel,
foreach, openxlsx, forcats, epitools, zoo, mvtnorm

Create Catseye Plots Illustrating the Normal Distribution of the
Means
person("Clark", "Andersen", email = "clanders@utmb.edu", role=c("cre","aut"))
Provides the tools to produce catseye plots, principally
by catseyesplot() function which calls R's standard plot() function internally, or alternatively
by the catseyes() function to overlay the catseye plot onto an existing
R plot window. Catseye plots illustrate the normal distribution of the mean (picture a
normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence
interval; they are an intuitive way of illustrating and comparing normally distributed estimates,
and are arguably a superior alternative to standard confidence intervals, since they show the full
distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require
pre-calculated means and standard errors (or standard deviations), provided as numeric vectors;
this allows the flexibility of obtaining this information from a variety of sources, such as
direct calculation or prediction from a model.  Catseye plots, as illustrations of the
normal distribution of the means, are described in Cumming (2013 & 2014).
Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.