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>.

Provides a lasso-based method for building mechanistic models using the SAMBA algorithm (Stochastic Approximation for Model Building Algorithm) (M Prague, M  Lavielle (2022) <doi:10.1002/psp4.12742>). The package extends the 'Rsmlx' package (version 2024.1.0) to better handle high-dimensional data. It relies on the 'Monolix' software (version 2024R1; see (<https://monolixsuite.slp-software.com/monolix/2024R1/>), which must be installed beforehand.

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>.

Bandwidth selection for kernel density estimators of 2-d
    level sets and highest density regions. It applies a plug-in
    strategy to estimate the asymptotic risk function and minimize to
    get the optimal bandwidth matrix. See Doss and Weng (2018)
    <arXiv:1806.00731> for more detail.