Download Data from the FAOSTAT Database of the Food and Agricultural Organization (FAO) of the United Nations.
    A list of functions to download statistics from FAOSTAT (database of the FAO <https://www.fao.org/faostat/>) 
    and WDI (database of the World Bank <https://data.worldbank.org/>), and to perform some harmonization operations.

Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally (Chalmers, 2015, <doi:10.1177/0146621615597894>; 
    Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, <doi:10.3389/fpsyg.2012.00055>).

Create Frequently Asked Questions page for Shiny application.

Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.

Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation
    File Format) files are like 'CSV' files, with a little bit of added
    meta information in a header and standardized NA values. They are
    quite often used for machine learning data sets and were introduced
    for the 'WEKA' machine learning 'Java' toolbox. See
    <https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/>
    for further info on 'ARFF' and for
    <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'.
    'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and
    it is at least a faster reader (for bigger files). It uses 'readr' as
    parser back-end for the data section of the 'ARFF' file. Consistency
    with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of
    'ARFF' files from 'OpenML'.