Showing 4 of total 4 results (show query)
quanteda
quanteda:Quantitative Analysis of Textual Data
A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Maintained by Kenneth Benoit. Last updated 3 months ago.
corpusnatural-language-processingquantedatext-analyticsonetbbcpp
851 stars 16.65 score 5.4k scripts 52 dependents
mllg
checkmate:Fast and Versatile Argument Checks
Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.
Maintained by Michel Lang. Last updated 8 months ago.
276 stars 16.32 score 1.5k scripts 2.0k dependents
poissonconsulting
checkr:Check the Properties of Common R Objects
Expressive, assertive, pipe-friendly functions to check the properties of common R objects. In the case of failure the functions issue informative error messages. Superseded by the 'chk' package.
Maintained by Joe Thorley. Last updated 3 months ago.
13 stars 7.18 score 258 scripts 1 dependents
ai4ci
interfacer:Define and Enforce Contracts for Dataframes as Function Parameters
A dataframe validation framework for package builders who use dataframes as function parameters. It performs checks on column names, coerces data-types, and checks grouping to make sure user inputs conform to a specification provided by the package author. It provides a mechanism for package authors to automatically document supported dataframe inputs and selectively dispatch to functions depending on the format of a dataframe much like S3 does for classes. It also contains some developer tools to make working with and documenting dataframe specifications easier. It helps package developers to improve their documentation and simplifies parameter validation where dataframes are used as function parameters.
Maintained by Robert Challen. Last updated 2 months ago.
2 stars 6.43 score 2 dependents