Tools for detecting cellwise outliers and robust methods to analyze
    data which may contain them. Contains the implementation of the algorithms described in
    Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access)
    Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access),
    Raymaekers and Rousseeuw (2022) <doi:10.1080/01621459.2023.2267777> (open access)
    Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access).
    Examples can be found in the vignettes:
    "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples",
    "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples",
    and "cellwise_weights_examples".

Implementation of the Coarsened Exact Matching algorithm discussed 
	along with its properties in
  Iacus, King, Porro (2011) <DOI:10.1198/jasa.2011.tm09599>;
	Iacus, King, Porro (2012) <DOI:10.1093/pan/mpr013> and
	Iacus, King, Porro (2019) <DOI:10.1017/pan.2018.29>.

'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).

Broken adaptive ridge estimator for censored data is used to
  select variables and estimate their coefficients in the semi-parametric 
  accelerated failure time model for right-censored survival data.

The network analysis plays an important role in numerous application domains including biomedicine. 
             Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. 
	           The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. 
	           Reference: 
             Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, <doi:10.1111/biom.13815>.