Provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different
  methods are also provided to calculate common metrics such as humification index and fluorescence index.

Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, and identify cut points. Also includes three example data 
    sets of administrative education records for learning how to process records 
    with errors.

Provides functions to create simulated time series of environmental
    exposures (e.g., temperature, air pollution) and health outcomes for use in
    power analysis and simulation studies in environmental epidemiology. This
    package also provides functions to evaluate the results of simulation studies
    based on these simulated time series. This work was supported by a grant
    from the National Institute of Environmental Health Sciences (R00ES022631) and
    a fellowship from the Colorado State University Programs for Research and
    Scholarly Excellence.

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).