plspm

R Package plspm: Tools for Partial Least Squares Path Modeling (PLS-PM). plspm contains a set of functions for performing Partial Least Squares Path Modeling (PLS-PM) analysis for both metric and non-metric data, as well as REBUS analysis.

Keywords for this software

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References in zbMATH (referenced in 10 articles )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Mehmetoglu, Mehmet; Venturini, Sergio: Structural equation modelling with partial least squares using Stata and R (2021)
  2. Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houée-Bigot: Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package (2020) not zbMATH
  3. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  4. Fattore, Marco; Pelagatti, Matteo; Vittadini, Giorgio: A least squares approach to latent variables extraction in formative-reflective models (2018)
  5. Mair, Patrick: Modern psychometrics with R (2018)
  6. Stéphanie Bougeard; Stéphane Dray: Supervised Multiblock Analysis in R with the ade4 Package (2018) not zbMATH
  7. Davino, Cristina; Vinzi, Vincenzo Esposito: Quantile composite-based path modeling (2016)
  8. Ringle, Christian M.; Sarstedt, Marko; Schlittgen, Rainer: Genetic algorithm segmentation in partial least squares structural equation modeling (2014)
  9. Armin Monecke; Friedrich Leisch: semPLS: Structural Equation Modeling Using Partial Least Squares (2012) not zbMATH
  10. Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)