The Reinert Method for Textual Data Clustering

---

Documentation for package ‘rainette’ version 0.3.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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