Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.

A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.

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.

Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().

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.