person("Wentao", "Zhu", role=c("aut"), email = "1306395569@qq.com"),

Title: Random Forest Classification with Parallel Computing
Authors@R: c(person("Bertram", "Ieong", role=c("aut","cre","cph"), email="contactme@bertramieong.com"))
Description: R package implementing random forest classification using parallel computing, built with Fortran and OpenMP in the backend.

Title: Support for Parallel Computation, Logging, and Function
Description: Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk,
and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in
Imports: snow, XML, jsonlite, methods, utils
Suggests: testthat, shiny, DT, knitr, rmarkdown

Suggests: mailR, testthat, shiny, DT, knitr, rmarkdown

Imports: snow, xml2, jsonlite, methods, utils

Suggests: mailR, testthat, shiny, DT, knitr, rmarkdown, tibble

Title: Paralellised Versions of Constraint Based Causal Discovery
Author: Thuc Duy Le, Tao Hoang, Shu Hu, and Liang Zhang
Maintainer: Thuc Duy Le <Thuc.Le@unisa.edu.au>
Suggests: bnlearn, pcalg, parallel, Rgraphviz
Description: Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the pcalg package but will be much more efficient.

Description: Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient.

Provides two functions: Group_function() and Parallel_Tree(). Group_function() applies a given function (e.g.,mean()) to input variable(s) by group across levels. Has additional data management options.
Parallel_Tree() uses 'ggplot2' to create a parallel coordinate plots (technically a facsimile of parallel coordinate plots in a Cartesian coordinate system).
Used in combination these functions can create parallel tree plots, a variant of parallel coordinate plots, which are useful for visualizing multilevel data.