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Classification with Parallel Factor Analysis

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8.5Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

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. Uses the 'clue' package to align Parafac or Parafac2 models across data splits in the cross-validation procedure. Calculates classification importance of individual features using permutation feature importance. Implements parallel computing via the 'foreach', 'doParallel', and 'doRNG' packages.

Install

Health

CRAN checks
13OK
Slowest check: 3.2 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-04-15
  • ERROR13 OK, 1 ERROR2026-03-30
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.3.3
READMENoVignettesYes · 1 · dynamicNEWSNoCitation fileNo
Help pages
10 (1 internal)
Examples that run
0%9 never run
Exported functions with examples
100%
Documented parameters
not tracked
Return-value docs
not tracked
Help pages citing references
90%

Downloads

8.5K
CRAN downloads in the past year
Rank #4,638 · ~23/day · ~710/mo
Daily download trend is not available in this view yet.
82230 days
1.8K90 days
8.5K1 year
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Also on197 r2u10 autocran

Repository

1Stars
0Forks
0Open issues
0Open PRs
6Releases
50Commits
1Commit identities
classificationcross-validationparafacparafac2
License file reads as GPL-3.0 · 50 commits · Last activity 2026-09-22 · 0% stars, 30d
Responsiveness
not measured

Stars over time

2026-07-07 · 02026-07-08 · 1
PracticesBeta

4 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/matthewasisgress/cpfa on 2026-10-18.

Continuous integration (1)
GitHub Actions
Docs source (3)
pkgdown

GitHub Pages site last deployed 2026-09-22 · The repository has a site deploy workflow

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
12 external dependencies (excludes base and recommended)

Installing cpfa adds 21 packages (12 with compiled code, 19 MB of source tarballs). Heaviest: data.table 5.7 MB, glmnet 2.7 MB, Rcpp 1.8 MB.

Depends (2)
R >= 4.3.0multiway
LinkingTo (0)
none
Suggests (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (1)
Author, Maintainer since 1.0-0 (2022)
Listed in earlier versions (3)
Show 3 peopleHide 3 people
Matthew A. Snodgress
no longer listed · 1.0-0 to 1.1-4
Matthew A.
no longer listed · 1.1-5 to 1.2-8
Asisgress
no longer listed · 1.2-9 to 1.3.3
Releases (34)

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Package metadata

First published
2022-03-09
Total releases
34 / 4 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 4.3.0
Download size
351 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("cpfa")
Asisgress, M. A. (2026). cpfa: Classification with Parallel Factor Analysis (Version 1.3.3) [Computer software]. https://doi.org/10.32614/CRAN.package.cpfa

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for cpfa version 1.3.3 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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