Sparse redundancy analysis
	for high dimensional (biomedical) data. Directional multivariate analysis 
	to express the maximum variance in the predicted data set by a linear 
	combination of variables of the predictive data set. Implemented in a 
	partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.

Provides you with easy, programmatic access to SRDP data.

Estimate average treatment effects (ATEs) in stratified randomized experiments. 'sreg' is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. 'sreg' computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) <doi:10.1080/01621459.2017.1375934>; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) <doi:10.48550/arXiv.2204.08356>; and Jiang, Linton, Tang, Zhang (2023+) <doi:10.48550/arXiv.2201.13004>.

It is a framework to fit semiparametric regression estimators for the total parameter of a finite population when the interest variable is asymmetric distributed. The main references for this package are Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) 
  Cardozo C.A, Paula G.A. and Vanegas L.H. (2022) "Generalized log-gamma additive partial linear mdoels with P-spline smoothing", Statistical Papers. 
  Cardozo C.A and Alonso-Malaver C.E. (2022). "Semi-parametric model assisted estimation in finite populations." In preparation.

Functions to perform split robust least angle regression. The approach first uses the
             least angle regression algorithm to split the variables into the models of an ensemble
             and robust estimates of the correlation between predictors. An elastic net estimator is 
             then applied to the selected predictors in each model using the imputed data from the
             detect deviating cell (DDC) method.