Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5).

Provides tools for the automatic stratification of survey
    populations using clustering and optimization techniques. The package
    assists researchers and survey practitioners in constructing
    homogeneous strata to improve the efficiency and precision of survey
    estimates. Functions are provided for generating strata, evaluating
    stratification quality, summarizing stratified populations, and
    visualizing stratification results. These tools support the design
    and implementation of efficient survey sampling strategies. The
    package utilizes standard statistical methods from survey sampling,
    clustering, and optimization for automatic stratification. Methods
    are described in Cochran (1977, ISBN:9780471162405) and Lohr (2021,
    ISBN:9780367354556).

Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. 
  Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.