Package: RTextTools 1.4.3

RTextTools: Automatic Text Classification via Supervised Learning

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 eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation.

Authors:Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, Wouter van Atteveldt

RTextTools_1.4.3.tar.gz
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RTextTools.pdf |RTextTools.html✨
RTextTools/json (API)

# Install 'RTextTools' in R:
install.packages('RTextTools', repos = c('https://lorenc5.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • NYTimes - A sample dataset containing labeled headlines from The New York Times.
  • USCongress - A sample dataset containing labeled bills from the United State Congress.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.89 score 1 stars 764 scripts 1.1k downloads 9 mentions 18 exports 44 dependencies

Last updated 5 years agofrom:95f6a1b20b. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-win-x86_64OKNov 12 2024
R-4.5-linux-x86_64OKNov 12 2024
R-4.4-win-x86_64OKNov 12 2024
R-4.4-mac-x86_64OKNov 12 2024
R-4.4-mac-aarch64OKNov 12 2024
R-4.3-win-x86_64OKNov 12 2024
R-4.3-mac-x86_64OKNov 12 2024
R-4.3-mac-aarch64OKNov 12 2024

Exports:classify_modelclassify_modelscreate_analyticscreate_containercreate_ensembleSummarycreate_matrixcreate_precisionRecallSummarycreate_scoreSummarycross_validategetStemLanguagesprint_algorithmsread_datarecall_accuracysummary.analyticssummary.analytics_virgintrain_modeltrain_modelswordStem

Dependencies:BHbitopscaToolsclassclicodetoolsdata.tablediagramdigeste1071foreachfuturefuture.applyglmnetglobalsiprediteratorsKernSmoothlatticelavalistenvMASSMatrixNLPnnetnumDerivparallellyprodlimprogressrproxyrandomForestRcppRcppEigenrlangrpartshapeslamSparseMSQUAREMsurvivaltautmtreexml2

Readme and manuals

Help Manual

Help pageTopics
an S4 class containing the analytics for a classified set of documents.analytics_virgin-class
an S4 class containing the analytics for a classified set of documents.analytics-class
converts a tm DocumentTermMatrix or TermDocumentMatrix into a matrix.csr representation.as.compressed.matrix
makes predictions from a train_model() object.classify_model
makes predictions from a train_models() object.classify_models
creates an object of class analytics given classification results.create_analytics
creates a container for training, classifying, and analyzing documents.create_container
creates a summary with ensemble coverage and precision.create_ensembleSummary
creates a document-term matrix to be passed into create_container().create_matrix
creates a summary with precision, recall, and F1 scores.create_precisionRecallSummary
creates a summary with the best label for each document.create_scoreSummary
used for cross-validation of various algorithms.cross_validate
Query the languages supported in this packagegetStemLanguages
an S4 class containing the training and classification matrices.matrix_container-class
a sample dataset containing labeled headlines from The New York Times.NYTimes
prints available algorithms for train_model() and train_models().print_algorithms
reads data from files into an R data frame.read_data
calculates the recall accuracy of the classified data.recall_accuracy
summarizes the 'analytics-class' classsummary.analytics
summarizes the 'analytics_virgin-class' classsummary.analytics_virgin
makes a model object using the specified algorithm.train_model
makes a model object using the specified algorithms.train_models
a sample dataset containing labeled bills from the United State Congress.USCongress
Get the common root/stem of wordswordStem