Package: FSelectorRcpp
Type: Package
Title: 'Rcpp' Implementation of 'FSelector' Entropy-Based Feature
        Selection Algorithms with a Sparse Matrix Support
Version: 0.1.8
Date: 2017-09-04
Authors@R: c(
 person("Zygmunt", "Zawadzki", role = c("aut", "cre"), email = "zygmunt@zstat.pl"),
 person("Marcin", "Kosinski", role = c("aut"), email = "m.p.kosinski@gmail.com"),
 person("Krzysztof", "Slomczynski", role = c("ctb"), email = "krzysztofslomczynski@gmail.com"),
 person("Damian", "Skrzypiec", role = c("ctb"), email = "damian.j.skrzypiec@@gmail.comm")
 )
Description: 'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support. It is also equipped with a parallel backend.
Depends: R (>= 3.3.2)
License: GPL-2
LazyData: TRUE
SystemRequirements: C++11
Imports: Rcpp (>= 0.12.12), foreach, iterators
LinkingTo: Rcpp, BH, RcppArmadillo, testthat
Suggests: testthat, Matrix, RcppArmadillo, dplyr, RWeka, entropy,
        FSelector, randomForest, doParallel, rpart, MASS, covr,
        parallel, htmltools, magrittr, knitr, RTCGA.rnaseq, ggplot2,
        microbenchmark, pbapply, tibble, rmarkdown, lintr
RoxygenNote: 6.0.1
BugReports: https://github.com/mi2-warsaw/FSelectorRcpp/issues
URL: http://mi2-warsaw.github.io/FSelectorRcpp/
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2017-09-04 18:52:51 UTC; Zygmunt
Author: Zygmunt Zawadzki [aut, cre],
  Marcin Kosinski [aut],
  Krzysztof Slomczynski [ctb],
  Damian Skrzypiec [ctb]
Maintainer: Zygmunt Zawadzki <zygmunt@zstat.pl>
Repository: CRAN
Date/Publication: 2017-09-05 10:12:14
Built: R 3.3.3; x86_64-w64-mingw32; 2017-12-11 22:28:07 UTC; windows
Archs: i386, x64
