Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. Additionally, StepReg features an interactive Shiny application to enhance usability and accessibility.

An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.

Software to simulate population change across space and time. Visintin et al. (2020) <doi:10.1111/2041-210X.13354>.

Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.

Functions to perform stepwise split regularized regression. The approach first 
             uses a stepwise algorithm to split the variables into the models with a goodness
             of fit criterion, and then regularization is applied to each model. The weights 
             of the models in the ensemble are determined based on a criterion selected by 
             the user.