An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides three algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides two algorithms for the next to the maximal eigenpair under
    these two cases.

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.