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>.

Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.