Package: EFA.dimensions
Type: Package
Title: Exploratory Factor Analysis Functions for Assessing
        Dimensionality
Version: 0.1.6
Date: 2020-07-16
Author: Brian P. O'Connor 
Maintainer: Brian P. O'Connor  <brian.oconnor@ubc.ca>
Description: Functions for seven different procedures for determining the number of 
    factors, including functions for parallel analysis and the minimum average partial 
    test. There are functions for conducting principal components analysis, principal 
    axis factor analysis, maximum likelihood factor analysis, image factor analysis, 
    and extension factor analysis, all of which can take raw data or correlation matrices 
    as input and with options for conducting the analyses using Pearson correlations, 
    Kendall correlations, Spearman correlations, or polychoric correlations. Varimax 
    rotation, promax rotation, and Procrustes rotations can be performed. Additional 
    functions focus on the factorability of a correlation matrix, the congruences between 
    factors from different datasets, and for assessing local independence. 
    O'Connor (2000, <doi:10.3758/bf03200807>);
    O'Connor (2001, <doi:10.1177/01466216010251011>);
    Fabrigar & Wegener (2012, ISBN:978-0-19-973417-7);
    Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2).
Imports: stats, psych, polycor
Suggests: lattice
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2020-07-16 08:38:29 UTC; brianoconnor
Repository: CRAN
Date/Publication: 2020-07-20 09:32:13 UTC
