The goal of this package is to provide wrapper functions in the 
    data cleaning and cleansing processes. These function helps in messages and 
    interaction with the user, keep track of information in pipelines, help in 
    the wrangling, munging, assessment and visualization of data frame-like 
    material.

Helps you imagine your data before you collect it. Hierarchical data structures
   and correlated data can be easily simulated, either from random number generators or
   by resampling from existing data sources. This package is faster with 'data.table' and
   'mvnfast' installed.

Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. 
    The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes.
    The package relies on the 'fabricjs' 'JavaScript' library. See <http://fabricjs.com/>.

We implement the Fast Covariance Estimation for 
             Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.

Collect  marketing data from facebook Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
    Use four spaces when indenting paragraphs within the Description.

Collect  your data on digital marketing campaigns from Facebook Leads Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Collect  your data on digital marketing campaigns from 'Facebook Organic' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Provides an interface to the 'Kairos' Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.

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.