Package: rainette 0.3.1.9000

rainette: The Reinert Method for Textual Data Clustering

An R implementation of the Reinert text clustering method. For more details about the algorithm see the included vignettes or Reinert (1990) <doi:10.1177/075910639002600103>.

Authors:Julien Barnier [aut, cre], Florian Privé [ctb]

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rainette.pdf |rainette.html✨
rainette/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/juba/rainette/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

text-analysistext-classification

6.90 score 55 stars 24 scripts 248 downloads 16 exports 85 dependencies

Last updated 7 months agofrom:7bbd2f2525. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-win-x86_64NOTENov 01 2024
R-4.5-linux-x86_64NOTENov 01 2024
R-4.4-win-x86_64NOTENov 01 2024
R-4.4-mac-x86_64NOTENov 01 2024
R-4.4-mac-aarch64NOTENov 01 2024
R-4.3-win-x86_64NOTENov 01 2024
R-4.3-mac-x86_64NOTENov 01 2024
R-4.3-mac-aarch64NOTENov 01 2024

Exports:clusters_by_doc_tablecutreecutree_rainettecutree_rainette2docs_by_cluster_tableimport_corpus_iramuteqmerge_segmentsrainetterainette_explorrainette_plotrainette_statsrainette2rainette2_complete_groupsrainette2_explorrainette2_plotsplit_segments

Dependencies:base64encbslibcachemclicolorspacecommonmarkcpp11crayoncurldendextenddigestdplyrfansifarverfastmapfastmatchfontawesomefsgenericsggplot2ggwordcloudgluegridExtragridtextgtablehighrhtmltoolshttpuvisobandISOcodesjpegjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrmarkdownMASSMatrixmemoisemgcvmimeminiUImunsellnlmensyllablepillarpkgconfigpngprogressrpromisesproxyCpurrrquantedaquanteda.textstatsR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenrlangRSpectrasassscalesshinySnowballCsourcetoolsstopwordsstringistringrtibbletidyrtidyselectutf8vctrsviridisviridisLitewithrxfunxml2xtableyaml

[en] Algorithms description

Julien Barnier

Rendered fromalgorithms_en.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2023-03-15
Started: 2022-02-17

[en] Introduction to rainette

Julien Barnier

Rendered fromintroduction_en.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2022-02-18
Started: 2020-03-09

[fr] Description des algorithmes

Julien Barnier

Rendered fromalgorithmes.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2023-03-15
Started: 2019-01-25

[fr] Utilisation de rainette

Julien Barnier

Rendered fromintroduction_usage.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2023-07-21
Started: 2019-01-24

Readme and manuals

Help Manual

Help pageTopics
Split a dtm into two clusters with reinert algorithmcluster_tab
Returns the number of segment of each cluster for each source documentclusters_by_doc_table
Cut a tree into groupscutree
Cut a rainette result tree into groups of documentscutree_rainette
Cut a rainette2 result object into groups of documentscutree_rainette2
Returns, for each cluster, the number of source documents with at least n segments of this clusterdocs_by_cluster_table
Import a corpus in Iramuteq formatimport_corpus_iramuteq
Merges segments according to minimum segment sizemerge_segments
return documents indices ordered by CA first axis coordinatesorder_docs
Corpus clustering based on the Reinert method - Simple clusteringrainette
Shiny gadget for rainette clustering explorationrainette_explor
Generate a clustering description plot from a rainette resultrainette_plot
Generate cluster keyness statistics from a rainette resultrainette_stats
Corpus clustering based on the Reinert method - Double clusteringrainette2
Complete groups membership with knn classificationrainette2_complete_groups
Shiny gadget for rainette2 clustering explorationrainette2_explor
Generate a clustering description plot from a rainette2 resultrainette2_plot
Remove features from dtm of each group base don cc_test and tsjselect_features
Split a character string or corpus into segmentssplit_segments split_segments.character split_segments.Corpus split_segments.corpus split_segments.tokens
Switch documents between two groups to maximize chi-square valueswitch_docs