person("Peter", "DeWitt", , "dewittpe@gmail.com", role = c("aut", "cre")),

Description: A collection of (wrapper) functions the creator found useful
for quickly placing data summaries and formatted regression results into
'.Rnw' or '.Rmd' files. Functions for generating commonly used graphics,
such as receiver operating curves or Bland-Altman plots, are also provided
by 'qwraps2'.  'qwraps2' is a updated version of a package 'qwraps'. The
original version 'qwraps' was never submitted to CRAN but can be found at
<https://github.com/dewittpe/qwraps/>. The implementation and limited scope
of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is

'qwraps2' is a updated version of an package 'qwraps'.  The original version

Suggests: survival, testthat, covr, rmarkdown

Imports: dplyr, ggplot2, knitr, Rcpp, tidyr, utils

Imports: devtools, dplyr, ggplot2, knitr, magrittr, Rcpp (>= 0.12.11),
Suggests: data.tree, survival, testthat, covr, rbenchmark, rmarkdown
LinkingTo: Rcpp (>= 0.12.11), RcppArmadillo

Imports: dplyr, ggplot2, knitr, magrittr, Rcpp (>= 0.12.11), rlang,
Suggests: survival, testthat, covr, rbenchmark, rmarkdown

person("Tell",  "Bennett", , "tell.bennett@cuanschutz.edu", role = c("ctb"))

Imports: dplyr (>= 1.0.0), ggplot2, knitr, Rcpp (>= 0.12.11), rlang,
Packaged: 2020-09-01 14:10:08 UTC; peterdewitt

Packaged: 2021-03-01 15:36:39 UTC; peterdewitt

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"))
Packaged: 2021-03-06 01:29:43 UTC; peterdewitt

Title: Bayesian Network Learning Improved Project
Authors@R: person("Mauro", "Scanagatta", email = "mauro@idsia.ch", role = c("aut", "cre"))
Description: 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.