Package: OptimalFactor 1.5.0

Jose Ventura-Leon

OptimalFactor: Item Selection in Factor Analysis by Boosting and Specification Search

Provides an iterative optimization algorithm for exploratory and confirmatory factor analysis that follows a boosting approach inspired by machine learning. It uses a composite fit index whose weights adapt to the degrees of freedom and the sample size, following Kenny and McCoach (2003) <doi:10.1207/S15328007SEM1003_1> and Shi, Lee and Maydeu-Olivares (2019) <doi:10.1177/0013164418783530>. It detects and removes problematic items (Heywood cases, cross-loadings, low loadings) with greedy or global search strategies, checks interfactor correlations, and supports ordinal data with WLSMV estimation and several rotation methods. A heuristic specification search for confirmatory models (MacCallum, 1986 <doi:10.1037/0033-2909.100.1.107>) explores move, drop and covariance operations across seed configurations. It also evaluates the resampling stability of the item selection, the recovery of a known or theoretical structure, and offers an optional conceptual analysis of removed items through a large language model service.

Authors:Jose Ventura-Leon [aut, cre]

OptimalFactor_1.5.0.tar.gz
OptimalFactor_1.5.0.zip(r-4.7-any)OptimalFactor_1.5.0.zip(r-4.6-any)
OptimalFactor_1.5.0.tar.gz(r-4.7-any)OptimalFactor_1.5.0.tar.gz(r-4.6-any)
OptimalFactor_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
OptimalFactor/json (API)

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

Bug tracker:https://github.com/jventural/optimalfactor/issues

Pkgdown/docs site:https://jventural.github.io

Datasets:

On CRAN:

Conda:

3.00 score 5 scripts 26 exports 39 dependencies

Last updated from:8bfdcbfe05. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK200
source / vignettesOK193
linux-releaseOK134
windows-develOK122
windows-releaseOK96
wasm-releaseOK123

Exports:algorithm_stabilitybifactor_indicescfa_boostingcross_validate_cfadiscriminant_boostingefa_boostingefa_optimizerexport_cfa_boostingexport_conceptual_analysisimprimir_items_eliminadositem_stabilitylocal_fit_searchoptimal_cfa_with_aioptimal_efa_with_aiprint_cfa_boostingprint_cfa_resultsprint_conceptual_analysisprint_efa_resultsredundancy_short_formreport_cfa_resultsreport_efa_resultssimulate_cfa_recoverysimulate_recoveryspecification_searchspecification_search_theorytheory_recovery

Dependencies:clicpp11dplyrfarvergenericsggplot2glueGPArotationgtableisobandlabelinglatticelavaanlifecyclemagrittrMASSmnormtnlmenumDerivpbivnormpillarpkgconfigpsychpurrrquadprogR6RColorBrewerrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

EFA-Boosting Algorithm
Algorithm Overview | How It Works | Step 1: Initial Model Fit | Step 2: Problem Detection | Step 3: Item Removal Strategy | Greedy Search (Default) | Global Search | Step 4: Adaptive Fit Calculation | Step 5: Convergence | Usage Examples | Basic Usage | Advanced Configuration | With AI Analysis | Parameters | Output
Rendered fromefa-boosting.Rmdusingknitr::rmarkdown

Last update: 2026-10-24
Started: 2026-10-24

Introduction to OptimalFactor
Overview | Installation | Main Features | EFA-Boosting Algorithm | CFA Optimization | Quick Start | Basic EFA-Boosting | Theoretical Background | References
Rendered fromOptimalFactor.Rmdusingknitr::rmarkdown

Last update: 2026-10-24
Started: 2026-10-24

Readme and manuals

Help Manual

Help pageTopics
Split-Half Stability of Any Item-Selection Algorithmalgorithm_stability
Bifactor Statistical Indicesbifactor_indices
CFA-Boosting Optimizationcfa_boosting
Split-Half Cross-Validation of a Factor Modelcross_validate_cfa
Expectativas DatasetData_Expectativas
Personality DatasetData_Personality
Discriminant-Boosting: Rescue the Discriminant Validity of a Scalediscriminant_boosting
EFA-Boosting Optimizationefa_boosting
Exploratory Factor Analysis Optimizer with Optional LLM Supportefa_optimizer
Export CFA Boosting Results to Structured Data Framesexport_cfa_boosting
Export LLM Conceptual Analyses to a UTF-8 Text Fileexport_conceptual_analysis
Impresión de Ítems Eliminadosimprimir_items_eliminados
Resampling Stability of the EFA-Boosting Item Selectionitem_stability print.item_stability
Local-Fit Search for a Unidimensional Modellocal_fit_search
Confirmatory Factor Analysis Optimization with AI Assistanceoptimal_cfa_with_ai
Exploratory Factor Analysis Optimization with AI Assistanceoptimal_efa_with_ai
Plot a CFA Recovery Simulationplot.simulate_cfa_recovery
Plot a Recovery Simulationplot.simulate_recovery
Print CFA-Boosting Resultsprint_cfa_boosting
Print Results from AI-Assisted CFA Refinementprint_cfa_results
Pretty-print LLM Conceptual Analyses for Removed/Retained Itemsprint_conceptual_analysis
Print Results from AI-Assisted EFA Refinementprint_efa_results
Print method for discriminant_boostingprint.discriminant_boosting
Print method for local_fit_searchprint.local_fit_search
Print method for specification_searchprint.specification_search
Redundancy-Guided Short Form of a Unidimensional Scaleredundancy_short_form
Report the CFA boosting resultsreport_cfa_results
Console Report for EFA Optimization Resultsreport_efa_results
Monte Carlo Recovery of a Known Structure with CFA-Boostingprint.simulate_cfa_recovery simulate_cfa_recovery
Monte Carlo Recovery of a Known Factor Structureprint.simulate_recovery simulate_recovery
Theory-Guided Specification Search for CFA Modelsspecification_search_theory
How Much of the Theoretical Structure a Solution Recoverstheory_recovery