# A Proposal for a New Python Library Implementing Stepwise Procedure
## 2 Background
### Computational Implementation of Stepwise  
This section briefly describes implementations in some of the main languages used in the statistical analysis and data modeling. The R language is widely known for its efficiency in statistical analyses and has a variety of packages that support the stepwise procedure [60]. The \"MASS\" package (v. 7.3-61) provides the \"stepAIC()\" function, which allows for the implementation of the stepwise forward, backward, and bidirectional methods. In addition, the \"leaps\", \"stepwise\", and \"caret\" packages also provide additional functionality for variable selection and statistical modeling. The stepwise procedure is available through the PROC REG command for users of SAS (v. 9.4M8), a statistical tool widely used in industry and research. This command allows the automatic selection of variables using different input methods, such as the forward, backward, or stepwise method. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Journal** & **Objective** \\ \hline
[9] & Mausam & Select statistically significant variables for forecasting storms \\
[10] & Landscape and Urban Planning & Select hedonic variables in modeling real estate sales prices \\
[12] & Poultry Science & Predict total egg production in European quail based \\
[13] & Ecosphere & To analyze the depth distribution of organic carbon in the soils of eastern Australia \\
[19] & Forensic Science International: Genetics & Estimation of chronological age using specific DNA methylation patterns \\
[17] & Environmental and Ecological Statistics & Relate deforestation and forest fragmentation metrics to socioeconomic and bio geophysical factors \\
[14] & Air Quality, Atmosphere and Health & Evaluate and provide hourly air quality forecasting \\
[18] & Clinical Pharmacokinetics & Predicting the Effect of Renal Failure on Pharmacokinetics of Drugs \\
[11] & Small Raminant Research & Predict marketable carcass characteristics and cuts of meat \\
[52] & Plant Methods & Evaluate the detection capacity of terrestrial light and range data in the estimation of corn biomass in precision agriculture practices \\
[15] & Ecological Indicators & Estimate the above-ground biomass indicator of degraded grasslands using machine learning methods \\
[53] & Land Use Policy & Apply and compare stepwise regression with the Random Forest algorithm for mass evaluation in an urban residential area \\
[54] & Applied Soft Computing & Predict mortality from COVID-19 \\
[55] & Hydrology and Earth System Sciences & Estimate the regionalization of hydrological model parameters \\
[56] & Building and Environment & To analyze the impacts of urbanization characteristics on carbon \\  & & emissions from road passenger transport \\
[57] & Sustainability (Switzerland) & To analyze the performance of stepwise multiple regression \\  & & methods to estimate the biomass distribution of eucalyptus \\
[21] & IEEE Engineering Management Review & Analyze the work–life balance of women in information \\
[58] & Applied Water Science & \\
[20] & Journal of Craniofacial Surgery & Predict changes in head index after cranioplasty \\
[59] & Information Systems and e-Business & Predict the influence of climate variations on the spread \\  & Management & of COVID-19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of stepwise applications in regression models. In MATLAB (v. R2023b), a programming platform commonly used in engineering and science, the \"Statistics and Machine Learning Toolbox\" package assists in implementing the stepwise procedure. This package provides functionality for adjusting regression models, including the stepwise method. In addition to the languages mentioned above, other programming languages implement the stepwise procedure. Julia, for example, offers the \"GLM\" (v. 1.9.0) and \"StatsModels.jl\" (v. 0.6.35) packages that provide functionality for stepwise regression.