Learn Group Bayesian Networks using Hierarchical Clustering
Learn group Bayesian Networks using hierarchical Clustering. This package implements the inference of group Bayesian networks based on hierarchical Clustering, and the adaptive refinement of the grouping regarding an outcome of interest.
R (>= 2.14.0), bnlearn, ClustOfVar, PCAmixdata, arules, zoo
PRROC, MLmetrics, rlist, stats, purrr, magrittr, visNetwork,
plyr, stringr

R (>= 3.5.0), bnlearn, ClustOfVar, PCAmixdata, arules, zoo
PRROC, MLmetrics, rlist, stats, magrittr, visNetwork, plyr,
stringr

Inferring Group Bayesian Networks using Hierarchical Feature
Clustering
c(person("Ann-Kristin", "Becker", email = "annkristinbecker@web.de", role = c("aut", "cre")),
person("Lars", "Kaderali", email="lars.kaderali@uni-greifswald.de", role=c("aut", "ths")))
Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.
Ann-Kristin Becker [aut, cre],
Lars Kaderali [aut, ths]

Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).

Performing computations related to groupseqential boundaries.
The computations are done with the alpha spending approach. Therefore
interim analyses need not to be equally spaced, and their number need not to be specified in advance.
It is appropriate for any trial based on normally distributed test statistics with independent increments,
survival studies, and certain longitudinal designs.
Among other things it computes boundaries, power, drift and confidence intervals.
The package provides this in a GUI and user has option of graphical output of the results
and saving them into .html files.
GNU GENERAL PUBLIC LICENSE - Version 2, June 1991