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.' (2018) <doi:10.1111/rssb.12275>) and SearchingSampling ('Guo' (2023) <doi:10.1093/jrsssb/qkad049>), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2018) <doi:10.1016/j.jeconom.2018.07.002>).

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.

Implements the Robust Scoring Equations estimator to fit linear
    mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.

Provides a comprehensive toolset for estimating Latent Profile 
    Analysis (LPA) models that are robust to multivariate outliers and missing 
    data. By integrating a high-performance 'C++' engine via 'RcppArmadillo' 
    with a Full Information Maximum Likelihood (FIML) approach and Huber 
    weighting, it reliably extracts latent profiles even in complex datasets. 
    It supports multiple geometric variance-covariance models, along with 
    functions for bootstrapped likelihood ratio tests and plotting.
    For methodological details on the Bootstrapped Likelihood Ratio Test, see
    Nylund et al. (2007) <doi:10.1080/10705510701575396>. For robust clustering 
    methods, see Garcia-Escudero et al. (2010) <doi:10.1007/s11634-010-0064-5>.