Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.

Provides comprehensive analytics, reporting, and testing capabilities 
    for systematic review search strategies. The package focuses on validating search 
    performance, generating standardized 'PRISMA'-compliant reports, and ensuring 
    reproducibility in evidence synthesis. Features include precision-recall analysis, 
    cross-database performance comparison, benchmark validation against gold standards, 
    sensitivity analysis, temporal coverage assessment, automated report generation,
    and statistical comparison of search strategies. Supports multiple export formats 
    including 'CSV', 'Excel', 'RIS', 'BibTeX', and 'EndNote'. Includes tools for duplicate 
    detection, search strategy optimization, cross-validation frameworks, meta-analysis 
    of benchmark results, power analysis for study design, and reproducibility package 
    creation. Optionally connects to 'PubMed' for direct database searching and real-time 
    strategy comparison using the 'E-utilities' 'API'. Enhanced with bootstrap comparison 
    methods, 'McNemar' test for strategy evaluation, and comprehensive visualization tools 
    for performance assessment. Methods based on Manning et al. (2008) 
    for information retrieval metrics, Moher et al. (2009)
    for 'PRISMA' guidelines, and Sampson et al. (2006) for 
    systematic review search methodology.

Provides a search interface to look up terms
    on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek',
    'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'.
    Upon searching, a browser window will open with the aforementioned search
    results.