Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.

Implements a general interface for model-based estimations
    for a wide variety of models, used in the computation of
    marginal means, contrast analysis and predictions. For a list of supported models,
    see 'insight::supported_models()'.

Fits the neighboring models of a fitted
  structural equation model and assesses the model
  uncertainty of the fitted model based on BIC posterior
  probabilities, using the method presented in
  Wu, Cheung, and Leung (2020)
  <doi:10.1080/00273171.2019.1574546>.

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.