# The Reinert Method for Textual Data Clustering

## Documentation for package ‘rainette’ version 0.3.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

clusters_by_doc_table | Returns the number of segment of each cluster for each source document
cluster_tab | Split a dtm into two clusters with reinert algorithm
cutree | Cut a tree into groups
cutree_rainette | Cut a rainette result tree into groups of documents
cutree_rainette2 | Cut a rainette2 result object into groups of documents
docs_by_cluster_table | Returns, for each cluster, the number of source documents with at least n segments of this cluster
import_corpus_iramuteq | Import a corpus in Iramuteq format
merge_segments | Merges segments according to minimum segment size
order_docs | return documents indices ordered by CA first axis coordinates
rainette | Corpus clustering based on the Reinert method - Simple clustering
rainette2 | Corpus clustering based on the Reinert method - Double clustering
rainette2_complete_groups | Complete groups membership with knn classification
rainette2_explor | Shiny gadget for rainette2 clustering exploration
rainette2_plot | Generate a clustering description plot from a rainette2 result
rainette_explor | Shiny gadget for rainette clustering exploration
rainette_plot | Generate a clustering description plot from a rainette result
rainette_stats | Generate cluster keyness statistics from a rainette result
select_features | Remove features from dtm of each group base don cc_test and tsj
split_segments | Split a character string or corpus into segments
split_segments.character | Split a character string or corpus into segments
split_segments.Corpus | Split a character string or corpus into segments
split_segments.corpus | Split a character string or corpus into segments
split_segments.tokens | Split a character string or corpus into segments
switch_docs | Switch documents between two groups to maximize chi-square value