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.

Robust parameter estimation and prediction of Gaussian stochastic 
 process emulators. It allows for robust parameter estimation and prediction using 
 Gaussian stochastic process emulator. It also implements the parallel partial 
 Gaussian stochastic process emulator for computer model with massive outputs 
 See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; 
 Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression. Specifically, the package implements robust least angle 
    regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), 
    (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; 
    <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares 
    regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).