Fit design-based linear and logistic elastic nets with complex survey data considering the sampling design when defining training and test sets using replicate weights. Methods implemented in this package are described in: A. Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.578>.

Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package.

Quickly and flexibly calculates weights for survey data, in order to correct
    for survey non-response or other sampling issues. Uses rake weighting, a common technique 
    also know as rim weighting or iterative proportional fitting.  This technique allows for weighting 
    on multiple variables, even when the interlocked distribution of the two
    variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in 
    Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking
    to the base weighting functionality in 'survey.'

This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.

An algorithm that trains a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute 
    combinations. This package works on top of the 'caret' package and proceeds in a 
    forward-step manner. More specifically, it builds and tests learners starting 
    from very few attributes until it includes a maximal number of attributes by 
    increasing the number of attributes at each step. Hence, for each fixed number
    of attributes, the algorithm tests various (randomly selected) learners and 
    picks those with the best performance in terms of training error. Throughout,
    the algorithm uses the information coming from the best learners at the previous
    step to build and test learners in the following step. In the end, it outputs
    a set of strong low-dimensional learners.