Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
The DESCRIPTION file:
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indscal fits the Individual Differences Scaling model.
mcr fits the Multiway Covariates Regression model.
parafac fits the 3-way and 4-way Parallel Factor Analysis-1 model.
parafac2 fits the 3-way and 4-way Parallel Factor Analysis-2 model.
sca fits the four different Simultaneous Component Analysis models.
tucker fits the 3-way and 4-way Tucker Factor Analysis model.
Nathaniel E. Helwig <[email protected]>
Maintainer: Nathaniel E. Helwig <[email protected]>
Bro, R., & De Jong, S. (1997). A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11, 393-401.
Bro, R., & Kiers, H.A.L. (2003). A new efficient method for determining the number of components in PARAFAC models. Journal of Chemometrics, 17, 274-286.
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Helwig, N. E. (2017). Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints. Biometrical Journal, 59(4), 783-803.
Helwig, N. E. (in prep). Constrained parallel factor analysis via the R package multiway.
Kiers, H. A. L., ten Berge, J. M. F., & Bro, R. (1999). PARAFAC2-part I: A direct-fitting algorithm for the PARAFAC2 model. Journal of Chemometrics, 13, 275-294.
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Timmerman, M. E., & Kiers, H. A. L. (2003). Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences. Psychometrika, 68, 105-121.
Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31, 279-311.
# See examples for indscal, mcr, parafac, parafac2, sca, and tucker
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