The elliptical factor model, as an extension of the traditional factor model,  effectively overcomes the limitations of the traditional model when dealing with heavy-tailed characteristic data. This package implements sparse principal component methods (SPC) and bi-sparse online principal component estimation (SPOC) for parameter estimation. Includes functionality for calculating mean squared error, relative error, and loading matrix sparsity.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Interact with the FRED API, <https://fred.stlouisfed.org/docs/api/fred/>, 
      to fetch observations across economic series;
      find information about different economic sources, releases, series, etc.;
      conduct searches by series name, attributes, or tags; and determine the 
      latest updates. Includes functions for creating panels of related variables 
      with minimal effort and datasets containing data sources, releases, and 
      popular FRED tags.

Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids
    in both mg/mL and mmol/L units.

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality
             and psychometric assessment. EGA estimates the number of dimensions in
	     	 psychological data using network estimation methods and community detection
             algorithms. A bootstrap method is provided to assess the stability of dimensions
	     	 and items. Fit is evaluated using the Entropy Fit family of indices. Unique 
             Variable Analysis evaluates the extent to which items are locally dependent (or
             redundant). Network loadings provide similar information to factor loadings and
	     	 can be used to compute network scores. A bootstrap and permutation approach are
             available to assess configural and metric invariance. Hierarchical structures
             can be detected using Hierarchical EGA. Time series and intensive longitudinal 
	     	 data can be analyzed using Dynamic EGA, supporting individual, group, and 
             population level assessments.