# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "OptimalFactor" in publications use:' type: software license: GPL-3.0-or-later title: 'OptimalFactor: Item Selection in Factor Analysis by Boosting and Specification Search' version: 1.5.0 abstract: 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) and Shi, Lee and Maydeu-Olivares (2019) . 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 ) 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: - family-names: Ventura-Leon given-names: Jose email: jventuraleon@gmail.com orcid: https://orcid.org/0000-0003-2996-4244 repository: https://cran.r-universe.dev repository-code: https://github.com/jventural/OptimalFactor commit: 8bfdcbfe058a911eb08f1824e9003822c6a46038 url: https://jventural.github.io/OptimalFactor/ date-released: '2026-10-05' contact: - family-names: Ventura-Leon given-names: Jose email: jventuraleon@gmail.com orcid: https://orcid.org/0000-0003-2996-4244