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EFAtools

The EFAtools package provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, implementations from R psych and SPSS can be compared. Moreover, functions for Schmid-Leiman transformation, and computation of omegas are provided. To speed up the analyses, some of the iterative procedures like principal axis factoring (PAF) are implemented in C++.

Installation

You can install the release version from CRAN with:

install.packages("EFAtools")

You can install the development version from GitHub with:

install.packages("devtools")
devtools::install_github("mdsteiner/EFAtools")

To also build the vignette when installing the development version, use:

install.packages("devtools")
devtools::install_github("mdsteiner/EFAtools", build_vignettes = TRUE)

Example

Here are a few examples on how to perform the analyses with the different types and how to compare the results using the COMPARE function. For more details, see the vignette by running vignette("EFAtools", package = "EFAtools"). The vignette provides a high-level introduction into the functionalities of the package.

# load the package
library(EFAtools)

# Run multiple factor retention methods
N_FACTORS(test_models$baseline$cormat, N = 500)
#> Warning in N_FACTORS(test_models$baseline$cormat, N = 500): ! 'x' was a correlation matrix but CD needs raw data. Skipping CD.
#> ℹ The default implementation of EKC has changed compared to EFAtools version <= 0.5.0 to reflect the original version by Braeken and van Assen (2017). The previous version (which often yields different results from the original) is available with type = 'AM2019'. See details in the help page.
#> 
#> ── Tests for the suitability of the data for factor analysis ───────────────────
#> 
#> Bartlett's test of sphericity
#> 
#> ✔ The Bartlett's test of sphericity was significant at an alpha level of .05.
#>   These data are probably suitable for factor analysis.
#> 
#>

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Version

Install

install.packages('EFAtools')

Monthly Downloads

1,405

Version

0.7.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/mdsteiner/EFAtools

Maintainer

Markus Steiner

Last Published

August 21st, 2026

Functions in EFAtools (0.7.1)

Kaiser-Meyer-Olkin criterion

Estimate factor scores for an EFA model

Next eigenvalue sufficiency test (NEST)

Various Factor Retention Criteria

Empirical Kaiser Criterion

Velicer's Minimum Average Partial (MAP) Criterion

Intelligence subtests from the Intelligence and Development Scales--2

Kaiser-Guttman Criterion

Hull method for determining the number of factors to retain

Sequential Chi Square Model Tests, RMSEA lower bound, and AIC

McDonald's omega

Parallel analysis

Schmid-Leiman Transformation

Rotate a loading matrix to a target using Procrustes alignment

Various outputs from SPSS (version 23) FACTOR

Various outputs from SPSS (version 27) FACTOR

Confidence intervals around mean

Covert a "LOADINGS" table to matrix or a matrix to "LOADINGS"

.average_matrices

Average a list of matrices elementwise

WJIV_ages_40_90

Woodcock Johnson IV: ages 40 to 90 plus

Woodcock Johnson IV: ages 3 to 5

WJIV_ages_14_19

Woodcock Johnson IV: ages 14 to 19

Compute explained variances from loadings

WJIV_ages_20_39

Woodcock Johnson IV: ages 20 to 39

Woodcock Johnson IV: ages 6 to 8

Woodcock Johnson IV: ages 9 to 13

.consensus_loss

Mean squared discrepancy to a consensus target

.consensus_target_procrustes_single

Internal single-start consensus engine

Compute number of non-matching indicator-to-factor correspondences

.extract_list_object

Extract a list object by its name

Format numbers for print method

.hyperplane_count

Count near-zero loadings

.oblique_procrustes

Oblique Procrustes target rotation using a k x k inner objective

Get reference values for nest.

Plot PARALLEL object

Plot KGC object

.orthogonal_procrustes

Closed-form orthogonal Procrustes rotation

Perform the iterative PAF procedure

plot.EFA_AVERAGE

Plot EFA_AVERAGE object

Plot EKC object

Plot HULL object

Parallel analysis on simulated data.

.stat_over_list

Calculate statistics for a list of matrices

Print BARTLETT object

Print function for CD objects

Print function for EKC objects

print.EFA_AVERAGE

Print EFA_AVERAGE object

Print function for PARALLEL objects

Print COMPARE object

Print SL object

print.SLLOADINGS

Print SLLOADINGS object

Print EFA object

Print function for SCREE objects

Print LOADINGS object

Print KMO object

Print OMEGA object

Print SMT object

Print function for HULL objects

Print function for KGC objects

print.N_FACTORS

Print function for N_FACTORS objects

.tucker_congruence

Tucker congruence between factors

Residuals function for EFA objects

population_models

population_models

Plot SCREE object

Print function for NEST objects

Print function for MAP objects

Four test models used in Grieder and Steiner (2020)

Compare two vectors or matrices (communalities or loadings)

Comparison Data

Bartlett's test of sphericity

EFAtools-package

EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

Exploratory factor analysis (EFA)

CONSENSUS_PROCRUSTES

Consensus Procrustes alignment across multiple loading matrices

Exploratory factor analysis on multiple data imputations

Model averaging across different EFA methods and types