Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, 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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cpd computes the N-way Canonical Polyadic Decomposition of a tensor.
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]>
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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.
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Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31, 279-311.
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# See examples for... # cpd (Canonical Polyadic Decomposition) # indscal (INividual Differences SCALing) # mcr (Multiway Covariates Regression) # parafac (Parallel Factor Analysis-1) # parafac2 (Parallel Factor Analysis-2) # sca (Simultaneous Component Analysis) # tucker (Tucker Factor Analysis)
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