Provides functions that allow for convenient working with vector space models of semantics/distributional semantic models/word embeddings. 
	Originally built for LSA models (hence the name), but can be used for all such vector-based models. 
	For actually building a vector semantic space, use the package 'lsa' or other specialized software. 
	Downloadable semantic spaces can be found at <https://sites.google.com/site/fritzgntr/software-resources>.

Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen
             und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, 
             C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen 
             der österreichischen Bildungsstandardüberprüfung." Wien: facultas. 
             (ISBN: 978-3-7089-1343-8, <https://www.iqs.gv.at/themen/bildungsforschung/publikationen/veroeffentlichte-publikationen>) zur 
             Verfügung.

Provides functions to simulate data from large-scale educational
  assessments, including background questionnaire data and cognitive item
  responses that adhere to a multiple-matrix sampled design. The theoretical
  foundation can be found on
  Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018)
  <doi:10.1186/s40536-018-0068-8>.

Functions for performing least-squares bilinear clustering of
    three-way data. The method uses the bilinear decomposition (or bi-additive
    model) to model two-way matrix slices while clustering over the third way.
    Up to four different types of clusters are included, one for each term of the
    bilinear decomposition. In this way, matrices are clustered simultaneously on
    (a subset of) their overall means, row margins, column margins and row-column
    interactions. The orthogonality of the bilinear model results in separability of
    the joint clustering problem into four separate ones. Three of these sub-problems
    are specific k-means problems, while a special algorithm is implemented for the
    interactions. Plotting methods are provided, including biplots for the low-rank
    approximations of the interactions.