Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
    of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
    model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).

Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.

Interval fusion and selection procedures for regression with 
             functional inputs. Methods include a semiparametric approach based
             on Sliced Inverse Regression (SIR), as described in 
             <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are 
             also included in the package) and a random forest based approach, 
             as described in <doi:10.1002/sam.11705>.

Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").