Linear cross-section factor model fitting with least-squares 
    and robust fitting the 'lmrobdetMM()' function from 'RobStatTM'; related
    volatility, Value at Risk and Expected Shortfall risk and performance 
    attribution (factor-contributed vs idiosyncratic returns);
    tabular displays of risk and performance reports;
    factor model Monte Carlo. The package authors would like to thank Chicago
    Research on Security Prices,LLC for the cross-section of about 300
    CRSP stocks data (in the data.table object 'stocksCRSP', and S&P GLOBAL MARKET
    INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and
    "factor exposures") fundamental data on the 300 companies in the data.table
    object 'factorSPGMI'.  The 'stocksCRSP' and 'factorsSPGMI' data are not covered by
    the GPL-2 license, are not provided as open source of any kind, and they are
    not to be redistributed in any form.

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>.