Contains logic for computing sparse principal components via the EESPCA method, 
    which is based on an approximation of the eigenvector/eigenvalue identity. 
    Includes logic to support execution of the TPower and rifle sparse PCA methods,
    as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle
    via cross-validation to minimize the out-of-sample reconstruction error.
    H. Robert Frost (2021) <doi:10.1080/10618600.2021.1987254>.

A set of functions to estimate capture probabilities and
  densities from multipass pass removal data.

Functions for eleven procedures for determining the number of 
    factors, including functions for parallel analysis and the minimum average partial 
    test. There are also 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, gamma 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, the assessment of local
    independence, the assessment of factor solution complexity, and internal consistency.
    Auerswald & Moshagen (2019, ISSN:1939-1463);
    Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2);
    Mulaik (2010, ISBN:978-1-4200-9981-2);
    O'Connor (2000, <doi:10.3758/bf03200807>);
    O'Connor (2001, ISSN:0146-6216).

Performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 
    <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 
    2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality
    of a set of variables using minimum rank factor analysis (see ten Berge & 
    Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package 
    also includes the option to compute minimum rank factor analysis by 
    itself, as well as the greater lower bound calculation.