Fits the neighboring models of a fitted
  structural equation model and assesses the model
  uncertainty of the fitted model based on BIC posterior
  probabilities (BPP), using the method presented in
  Wu, Cheung, and Leung (2020)
  <doi:10.1080/00273171.2019.1574546>. See
  Pesigan, Cheung, Wu, Chang, and Leung (2026)
  <doi:10.3758/s13428-025-02921-x> for an introduction to
  the package.

This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.

Audits predictions from fitted machine learning models without
    requiring retraining or access to the fitted model object. It supports
    binary classification, multiclass classification, and regression,
    providing performance metrics with bootstrap confidence intervals,
    calibration and threshold analyses, residual diagnostics, subgroup
    comparisons, user-defined acceptance criteria, risk warnings, and
    self-contained reports. For binary-classification and regression models,
    the package can also assemble and render structured model cards documenting
    intended use, evaluation data, performance, subgroup results, assumptions,
    and limitations. The model-card reporting approach is described by
    Mitchell et al. (2019) "Model Cards for Model Reporting"
    <doi:10.1145/3287560.3287596>.