alishinski/clustRcompaR: Easy Interface for Clustering a Set of Documents and Exploring Group- Based Patterns

Provides an interface to perform cluster analysis on a corpus of text. Interfaces to Quanteda to assemble text corpuses easily. Deviationalizes text vectors prior to clustering using technique described by Sherin (Sherin, B. [2013]. A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences, 22(4), 600-638. Chicago. http://dx.doi.org/10.1080/10508406.2013.836654). Uses cosine similarity as distance metric for two stage clustering process, involving Ward's algorithm hierarchical agglomerative clustering, and k-means clustering. Selects optimal number of clusters to maximize "variance explained" by clusters, adjusted by the number of clusters. Provides plotted output of clustering results as well as printed output. Assesses "model fit" of clustering solution to a set of preexisting groups in dataset.

install.packages("remotes")
remotes::install_github("alishinski/clustRcompaR")