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>).

Inference for the treatment effect with possibly invalid instrumental variables via TSHT('Guo et al.' (2016) <arXiv:1603.05224>) and SearchingSampling('Guo' (2021) <arXiv:2104.06911>), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2016) <arXiv:1609.06713>).

Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.

Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.