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.

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.

An implementation of sparsity-ranked lasso for 
    time series data. This methodology is especially useful for 
    large time series with exogenous features and/or complex 
    seasonality. Originally described in Peterson and Cavanaugh 
    (2022) <doi:10.1007/s10182-021-00431-7> in the context of variable 
    selection with interactions and/or polynomials, ranked sparsity is 
    a philosophy with methods useful for variable selection in the 
    presence of prior informational asymmetry. This situation exists for time 
    series data with complex seasonality, as shown in Peterson and Cavanaugh 
    (2023+) <doi:10.48550/arXiv.2211.01492>, which also describes this package
    in greater detail. The Sparsity-Ranked Lasso (SRL) for Time Series  
    implemented in 'srlTS' can fit large/complex/high-frequency time series
    quickly, even with a high-dimensional exogenous feature set. The SRL is
    considerably faster than its competitors, while often producing more 
    accurate predictions. Also included is a long hourly series of arrivals 
    into the University of Iowa Emergency Department with concurrent local 
    temperature.