R (>= 3.1.3), testthat (>= 1.0.0), methods, XML, stringr

c(
person("Sebastian", "Wolf", , "sebastian@mail-wolf.de", role = c("aut","cre")),
person("Matthias", "Pfeifer", , "matthiaspfeifer@gmx.net", role = c("aut", "ctb")),
person("Sergej", "Potapov", , "sergej.potapov@roche.com", role = c("ctb")),
person("Roche", role = c("cph", "fnd"))
)
R (>= 3.1.3), testthat (>= 1.0.0), magick(>= 1.3), methods,
XML, stringr

R (>= 3.1.3), testthat (>= 1.0.0), magick(>= 1.3), methods,
XML, stringr, magrittr
RTest-package.R utils.R utils.examples.R utils.test.R
utils.xmlRead.R utils.xmlWrite.R RTestCollection.R RTestCase.R
RTestCase.test-adapter.R

base64, jsonlite, rlang, glue, magrittr, stringr, tcltk
RTest-package.R utils.R utils.examples.R utils.test.R
utils.xmlRead.R utils.xmlWrite.R RTestCollection.R RTestCase.R
RTestCase.test-adapter.R utils.testthat.R

base64, jsonlite, rlang, glue, magrittr, stringr

Automatic Text Classification via Supervised Learning
Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun,
Emiliano Grossman, Wouter van Atteveldt
R (>= 2.13.0), methods, SparseM, randomForest, tree, nnet, tm,
e1071, Rstem, ipred, caTools, maxent, RODBC, glmnet
RTextTools is a machine learning package for automatic
text classification that makes it simple for novice users to
get started with machine learning, while allowing experienced
users to easily experiment with different settings and
algorithm combinations. The package includes nine algorithms
for ensemble classification (SVM, SLDA, BOOSTING, BAGGING, RF,
GLMNET, TREE, NNET, MAXENT), comprehensive analytics, and
thorough documentation.

R (>= 2.13.0), methods, SparseM, randomForest, tree, nnet, tm,
e1071, ipred, caTools, maxent, glmnet, Rstem

Timothy P. Jurka,  Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, Wouter van Atteveldt
R (>= 2.13.0), methods, SparseM, randomForest, tree, nnet, tm,
e1071, ipred, caTools, maxent, glmnet, Rstem, tau
RTextTools is a machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. The package includes nine algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks, maximum entropy), comprehensive analytics, and thorough documentation.