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robustfa  

Object Oriented Solution for Robust Factor Analysis
View on CRAN: Click here


Download and install robustfa package within the R console
Install from CRAN:
install.packages("robustfa")

Install from Github:
library("remotes")
install_github("cran/robustfa")

Install by package version:
library("remotes")
install_version("robustfa", "1.2-0")



Attach the package and use:
library("robustfa")
Maintained by
Frederic Bertrand
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-03-12
Latest Update: 2025-09-03
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) ). 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) ) (Todorov, V. and Filzmoser, P. (2009) ), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes.
How to cite:
Frederic Bertrand (2012). robustfa: Object Oriented Solution for Robust Factor Analysis. R package version 1.2-0, https://cran.r-project.org/web/packages/robustfa. Accessed 04 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:52), 1.0-01 (2012-05-11 14:50), 1.0-02 (2012-06-18 09:30), 1.0-03 (2012-07-06 17:41), 1.0-4 (2013-10-20 12:06), 1.0-5 (2013-11-12 15:02), 1.0 (2012-03-12 11:16), 1.1-0 (2023-04-16 16:40)
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