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>).

Implements Factor Analytic Profile Analysis of Ipsatized Data
    ('FAPA'), a metric inferential framework for pattern detection and
    person-level reconstruction in multivariate profile data.  After
    row-centering (ipsatization) to remove profile elevation, 'FAPA' applies
    singular value decomposition ('SVD') to recover shared core profiles and
    individual pattern weights.  Dimensionality is determined by a
    variance-matched Horn's parallel analysis.  A three-stage bootstrap
    verification framework assesses (1) dimensionality via parallel analysis,
    (2) subspace stability via Procrustes principal angles, and (3) profile
    replicability via Tucker's congruence coefficients.  BCa bootstrap
    confidence intervals for core-profile coordinates are computed via the
    canonical 'boot' package implementation of Davison and Hinkley (1997)
    <doi:10.1017/CBO9780511802843>.

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>.