Supports teaching methods of estimating and testing time series
    factor models for use in robust portfolio construction and analysis. Unique
    in providing not only classical least squares, but also modern robust model
    fitting methods which are not much influenced by outliers. Includes
    returns and risk decompositions, with user choice of  standard deviation,
    value-at-risk, and expected shortfall risk measures. "Robust Statistics
    Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, 
    M. Salibian-Barrera (2019) <doi:10.1002/9781119214656>.

We present 'FACT' (Feature Attributions for ClusTering), a
  framework for unsupervised interpretation methods that can be used with an 
  arbitrary clustering algorithm. The package is capable of re-assigning instances to
  clusters (algorithm agnostic), preserves the integrity of the data and does
  not introduce additional models. 'FACT' is inspired by the principles of
  model-agnostic interpretation in supervised learning. Therefore, some of the
  methods presented are based on 'iml', a R Package for Interpretable Machine
  Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018)
  <doi:10.21105/joss.00786>.

Implements instrumental variable estimators for 2^K factorial experiments with noncompliance.

Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.

Some functions of 'ade4' and 'stats' are combined in order to obtain 
             a partition of the rows of a data table, with columns representing 
             variables of scales: quantitative, qualitative or frequency. 
             First, a principal axes method is performed and then, a combination 
             of Ward agglomerative hierarchical classification and K-means is 
             performed, using some of the first coordinates obtained from the 
             previous principal axes method. 
             In order to permit different weights of the elements to be clustered, 
             the function 'kmeansW', programmed in C++, is included. 
             It is a modification of 'kmeans'. Some graphical functions include 
             the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' 
             and 'ggrepel' packages to avoid  the super-position of the labels.