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  "Description": "Implements a multi-path forward selection algorithm using\nthe Akaike Information Criterion (AIC) for both linear\n(Gaussian) and logistic (binomial) regression models. Unlike\ntraditional stepwise methods that follow a single path, this\napproach explores multiple competitive model paths\nsimultaneously by retaining near-optimal models at each step.\nThe package includes stability estimation via bootstrap\nresampling and identifies plausible models based on AIC\ntolerance and variable stability thresholds. The information\ncriterion is described by Akaike (1974)\n<doi:10.1109/TAC.1974.1100705>, and bootstrap resampling by\nEfron (1979) <doi:10.1214/aos/1176344552>.",
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      "author": "Michael Obuobi",
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        "Introduction",
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        "Motivation",
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        "Algorithm Details",
        "Input Parameters",
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        "Problem-Specific Adjustments",
        "Statistical Justification",
        "Why AIC?",
        "Why Delta = 2?",
        "Why Bootstrap Stability?",
        "Why tau = 0.6?",
        "Advantages Over Traditional Methods",
        "vs. Single-Path Forward Selection",
        "vs. All-Subsets Regression",
        "vs. Lasso/Ridge Regression",
        "Practical Recommendations",
        "Workflow",
        "Reporting Results",
        "Limitations and Considerations",
        "Computational Complexity",
        "Model Family",
        "Assumptions",
        "References",
        "Appendix: Mathematical Details",
        "AIC Derivation",
        "Stability Score Estimation",
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