Package: facomplex 0.0.3

facomplex: Methods for Assessing Factor Complexity in Factor Analysis Solutions

Provides methods for estimating factor complexity coefficients in exploratory and confirmatory factor analysis (EFA/CFA) results. Included indices are the Hofman coefficient, Fleming's approach for factor simplicity, and others. Additional outputs include descriptive statistics (minimum, maximum, and mean) for target and non-target loadings, and visualization of results. References: Fleming, J.S. (2003) <doi:10.3758/bf03195531>; Hofmann, R.J. (1978) <doi:10.1207/s15327906mbr1302_9>; Kaiser, H.F. (1974) <doi:10.1007/BF02291575>; Bentler, P.M. (1977) <doi:10.1007/BF02294054>; Lorenzo-Seva, U. (2003) <doi:10.1007/BF02296652>.

Authors:Merino-Soto Cesar A. [aut, cre], Dominguez-Lara Sergio [ctb]

facomplex_0.0.3.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
facomplex/json (API)

# Install 'facomplex' in R:
install.packages('facomplex', repos = c('https://cmerinos.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cmerinos/facomplex/issues

Datasets:
  • fullclean - Fullclean: Data frame of responses to motivations for conducting research

On CRAN:

Conda:

confirmatory-factor-analysisesemexploratory-factor-analysisexploratory-structural-equations-modelingfactor-analysisfactor-complexityitem-analysispsychometric-analysis

5.30 score 2 stars 10 exports 17 dependencies

Last updated from:93838d9817. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK172
source / vignettesOK189
linux-release-x86_64OK174
macos-release-arm64OK168
macos-oldrel-arm64OK154
windows-develOK72
windows-releaseOK70
windows-oldrelOK65
wasm-releaseOK120

Exports:BSIentropyFLHofmannHofmannFacKCLSIglobalplotFacomplexprofileFacomplexsimloadSSindices

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Example 2: Assessing Factor Simplicity from psych::fa() Output
Step 1: Load data from psych | Step 2: Fit a 2-factor exploratory model | Step 3: View and save the loading matrix | Step 4: Compute complexity and simplicity indices | Hofmann Index | Bentler’s Simplicity Index | Kaiser-Cerny (KC) Criterion | Factor Simplicity Index (FSI)
Rendered fromexample2_facomplex.Rmdusingknitr::rmarkdown

Last update: 2026-06-23
Started: 2025-04-12

Exploring Factor Complexity with ESEM: Example 1
Example 1: Estimating Factor Complexity in an ESEM Solution | 1. Load the data | 2. Create a target matrix | 3. Specify the ESEM model | 4. Fit the ESEM model | 5. Compute factor complexity indices | Factor Simplicity Index (FSI) | Hofmann’s Index | Bentler’s Simplicity Index (BSI)
Rendered fromintro_facomplex.Rmdusingknitr::rmarkdown

Last update: 2026-06-23
Started: 2025-04-12