| multiblock | R Documentation | 
A collection of methods for analysis of data sets with more than two blocks of data.
Unsupervised methods:
 SCA - Simultaneous Component Analysis (sca)
 GCA - Generalized Canonical Analysis (gca)
 GPA - Generalized Procrustes Analysis (gpa)
 MFA - Multiple Factor Analysis (mfa)
 PCA-GCA (pcagca)
 DISCO - Distinctive and Common Components with SCA (disco)
 HPCA - Hierarchical Principal component analysis (hpca)
 MCOA - Multiple Co-Inertia Analysis (mcoa)
 JIVE - Joint and Individual Variation Explained (jive)
 STATIS - Structuration des Tableaux à Trois Indices de la Statistique (statis)
 HOGSVD - Higher Order Generalized SVD (hogsvd)
Design based methods:
 ASCA - Anova Simultaneous Component Analysis (asca)
Supervised methods:
 MB-PLS - Multiblock Partial Least Squares (mbpls)
 sMB-PLS - Sparse Multiblock Partial Least Squares (smbpls)
 SO-PLS - Sequential and Orthogonalized PLS (sopls)
 PO-PLS - Parallel and Orthogonalized PLS (popls)
 ROSA - Response Oriented Sequential Alternation (rosa)
 mbRDA - Multiblock Redundancy Analysis (mbrda)
Complex methods:
 L-PLS - Partial Least Squares in L configuration (lpls)
 SO-PLS-PM - Sequential and Orthogonalised PLS Path Modelling (sopls_pm)
Single- and two-block methods:
 PCA - Principal Component Analysis (pca)
 PCR - Principal Component Regression (pcr)
 PLSR - Partial Least Squares Regression (plsr)
 CCA - Canonical Correlation Analysis (cca)
 IFA - Interbattery Factor Analysis (ifa)
 GSVD - Generalized SVD (gsvd)
Datasets:
 Sensory assessment of candies (candies)
 Sensory, rheological, chemical and spectroscopic analysis of potatoes (potato)
 Data simulated to have certain characteristics (simulated)
 Wines of Val de Loire (wine)
Utility functions:
 Block-wise indexable data.frame (block.data.frame)
 Dummy-code a vector (dummycode)
Maintainer: Kristian Hovde Liland kristian.liland@nmbu.no (ORCID)
Other contributors:
Solve Sæbø [contributor]
Stefan Schrunner [reviewer]
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
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