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.

Provides two important functions for producing Gain chart and Lift chart for any classification model.

Data sets used for demonstrating or testing model-related
    packages are contained in this package.

More data sets used for demonstrating or testing model-related 
    packages are contained in this package. The data sets are downloaded and
    cached, allowing for more and bigger data sets.

Uses 'dplyr' and 'tidyeval' to fit statistical models inside
    the database. It currently supports KMeans and linear regression
    models.

Website generator with HTML summaries for predictive models.
    This package uses 'DALEX' explainers to describe global model behavior. 
    We can see how well models behave (tabs: Model Performance, Auditor),
    how much each variable contributes to predictions (tabs: Variable Response) 
    and which variables are the most important for a given model (tabs: Variable Importance).
    We can also compare Concept Drift for pairs of models (tabs: Drifter).
    Additionally, data available on the website can be easily recreated in current R session.
    Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 
    at Warsaw University of Technology, Faculty of Mathematics and Information Science.