dplyr, ggplot2, knitr, Rcpp, tidyr, utils

devtools, dplyr, ggplot2, knitr, magrittr, Rcpp (>= 0.12.11),
rlang, tidyr, utils
data.tree, survival, testthat, covr, rbenchmark, rmarkdown

dplyr, ggplot2, knitr, magrittr, Rcpp (>= 0.12.11), rlang,
tidyr, utils
survival, testthat, covr, rbenchmark, rmarkdown

c(
person("Peter", "DeWitt", , "dewittpe@gmail.com", role = c("aut", "cre")),
person("Tell",  "Bennett", , "tell.bennett@cuanschutz.edu", role = c("ctb"))
)

dplyr (>= 1.0.0), ggplot2, knitr, Rcpp (>= 0.12.11), rlang,
utils, xfun

c(
person(given = "Peter", family = "DeWitt",  email = "dewittpe@gmail.com",          role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6391-0795")),
person(given = "Tell",  family = "Bennett", email = "tell.bennett@cuanschutz.edu", role = c("ctb"),        comment = c(ORCID = "0000-0003-1483-4236"))
)

Bayesian Network Learning Improved Project
person("Mauro", "Scanagatta", email = "mauro@idsia.ch", role = c("aut", "cre"))
Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.

Perform JIVE Decompositions for Multi-Source Data
Performs the JIVE decompositions on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.