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. See Harshman and Lundy (1994):
<doi:10.1016/0167-9473(94)90132-5>. Classification using principal component
analysis (PCA) fit to a two-way data matrix is also supported. Uses component
weights from one mode of a Parafac, Parafac2, or PCA model as features to tune
parameters for one or more classification methods via a k-fold cross-validation
procedure. Allows for constraints on different tensor modes. Allows for
inclusion of additional features alongside features generated by the component
model. Supports penalized logistic regression, support vector machine, random
forest, feed-forward neural network, regularized discriminant analysis, and
gradient boosting machine. Supports binary and multiclass classification.
Predicts class labels or class probabilities, and calculates multiple
classification performance measures. Implements parallel computing via the
'foreach', 'doParallel', and 'doRNG' packages.
| Version: |
1.2-9 |
| Depends: |
R (≥ 4.3.0), multiway |
| Imports: |
glmnet, e1071, randomForest, nnet, rda, xgboost, foreach, doParallel, doRNG |
| Suggests: |
knitr, rmarkdown |
| Published: |
2026-05-08 |
| DOI: |
10.32614/CRAN.package.cpfa |
| Author: |
Matthew A. Asisgress
 [aut, cre] |
| Maintainer: |
Matthew A. Asisgress <mattgress at protonmail.ch> |
| BugReports: |
https://github.com/matthewasisgress/cpfa/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/matthewasisgress/cpfa |
| NeedsCompilation: |
no |
| Materials: |
ChangeLog |
| CRAN checks: |
cpfa results |
Documentation:
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