Package: robustfa
Type: Package
Title: Object Oriented Solution for Robust Factor Analysis
Version: 1.2-0
Date: 2025-09-10
Authors@R: c(
	person(given = "Frederic", family= "Bertrand", role = c("cre"), email = "frederic.bertrand@lecnam.net", comment = c(ORCID = "0000-0002-0837-8281")),
	person(given = "Ying-Ying", family= "Zhang (Robert)", role = c("aut")))
Author: Frederic Bertrand [cre]
        (<https://orcid.org/0000-0002-0837-8281>), Ying-Ying Zhang
        (Robert) [aut]
Maintainer: Frederic Bertrand <frederic.bertrand@lecnam.net>
Description: Outliers virtually exist in any datasets of any
        application field. To avoid the impact of outliers, we need to
        use robust estimators. Classical estimators of multivariate
        mean and covariance matrix are the sample mean and the sample
        covariance matrix. Outliers will affect the sample mean and the
        sample covariance matrix, and thus they will affect the
        classical factor analysis which depends on the classical
        estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and
        Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). So it is
        necessary to use the robust estimators of the sample mean and
        the sample covariance matrix. There are several robust
        estimators in the literature: Minimum Covariance Determinant
        estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum
        Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way
        to make multivariate analysis more robust is to replace the
        sample mean and the sample covariance matrix of the classical
        estimators to robust estimators (Maronna, R.A., Martin, D. and
        Yohai, V. (2006) <doi:10.1002/0470010940>) (Todorov, V. and
        Filzmoser, P. (2009) <doi:10.18637/jss.v032.i03>), which is our
        choice of robust factor analysis. We created an object oriented
        solution for robust factor analysis based on new S4 classes.
License: GPL (>= 2)
Depends: rrcov, R (>= 2.15.0)
Imports: methods, stats4, stats
Suggests: grid, lattice, cluster, mclust, MASS, ellipse, knitr,
        rmarkdown, testthat (>= 3.0.0)
LazyLoad: yes
LazyData: yes
Encoding: UTF-8
RoxygenNote: 7.3.2
VignetteBuilder: knitr, rmarkdown
URL: https://fbertran.github.io/robustfa/,
        https://github.com/fbertran/robustfa/
BugReports: https://github.com/fbertran/robustfa/issues/
Config/testthat/edition: 3
Repository: https://fbertran.r-universe.dev
Date/Publication: 2025-09-24 09:55:08 UTC
RemoteUrl: https://github.com/fbertran/robustfa
RemoteRef: HEAD
RemoteSha: bceeb0aa54149fab4821ed62a9bc122f72db3237
NeedsCompilation: no
Packaged: 2026-06-21 07:53:45 UTC; root