cpfa: Classification with Parallel Factor Analysis

Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array/tensor. See Harshman and Lundy (1994): <doi:10.1016/0167-9473(94)90132-5>. Uses component weights from one mode of the Parafac or Parafac2 model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Supports penalized logistic regression, support vector machine, random forest, and feed-forward neural network. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Parallel computing is implemented via the 'parallel' and 'doParallel' packages.

Version: 1.0-6
Depends: multiway, glmnet, e1071, randomForest, nnet
Imports: foreach, doParallel
Published: 2023-09-07
Author: Matthew A. Snodgress
Maintainer: Matthew A. Snodgress <snodg031 at umn.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: cpfa results


Reference manual: cpfa.pdf


Package source: cpfa_1.0-6.tar.gz
Windows binaries: r-devel: cpfa_1.0-6.zip, r-release: cpfa_1.0-6.zip, r-oldrel: cpfa_1.0-6.zip
macOS binaries: r-release (arm64): cpfa_1.0-6.tgz, r-oldrel (arm64): cpfa_1.0-6.tgz, r-release (x86_64): cpfa_1.0-6.tgz, r-oldrel (x86_64): cpfa_1.0-6.tgz
Old sources: cpfa archive


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