{FAfA}
Install {FAfA} together with its dependencies from
CRAN:
install.packages("FAfA")The development version can be installed in the same way with
remotes:
remotes::install_github("AFarukKILIC/FAfA", dependencies = TRUE)You can launch the application by running:
FAfA::run_app()Version 1.3 includes internal split-sample replication analysis for
EFA. The module divides the data reproducibly using a user-defined seed,
fits the same factor model in both halves, aligns factor labels and
signs, and compares each item’s primary factor and loading magnitude. A
squared loading difference of .04 or more is flagged as
volatile following Osborne and Fitzpatrick (2012).
Method reference: Osborne, J. W., & Fitzpatrick, D. C. (2012). Replication analysis in exploratory factor analysis: What it is and why it makes your analysis better. Practical Assessment, Research, and Evaluation, 17, Article 15. https://doi.org/10.7275/h0bd-4d11
FAfA is distributed under the GNU Affero General Public License,
version 3. The Dynamic Fit Index integration acknowledges Melissa G.
Wolf and Daniel McNeish and their AGPL-3 dynamic R package,
version 1.1.0. The integration was rewritten for FAfA rather than copied
verbatim. See inst/COPYRIGHTS for the complete third-party
notice. The complete FAfA source code is available from https://github.com/AFarukKILIC/FAfA.
DFI method reference: McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61-88. https://doi.org/10.1037/met0000425
From a source checkout, run the complete package check with:
source("dev/check_package.R")
check_fafa()This builds the source archive and runs
R CMD check --as-cran, including incoming feasibility
checks, suggested dependencies, and the PDF manual. The archive and
check logs are saved in build/. Browser tests can be run
separately with dev/run_ui_tests.R.