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.

Provides a unified framework for diagnosing common issues in statistical models including linear models, generalized linear models (logistic and Poisson regression), and survival models. Implements tests for multicollinearity, heteroscedasticity, autocorrelation, normality, influential observations, overdispersion, zero-inflation, and proportional hazards assumptions. Includes visualization methods for graphical diagnostics. Methods are based on established approaches including Fox and Monette (1992) <doi:10.1080/01621459.1992.10475190>, Breusch and Pagan (1979) <doi:10.2307/1911963>, and Dean and Lawless (1989) <doi:10.1080/01621459.1989.10478792>.

Generates 'DiagrammeR' model diagrams for hierarchical linear mixed effects models. Details can be found in Linse (2026) <doi:10.6339/26-JDS1222>.

An developer focused, low dependency package in 'tidymodels' that
    provides functions to register how models are to be used. Functions to
    register models are complimented with accessor functions to retrieve
    registered model information to aid in model fitting and error handling.

Defines classes and methods to learn models and use them
  to predict binary outcomes.  These are generic tools, but we also
  include specific examples for many common classifiers.

Local linear estimation of psychometric functions. Provides functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals.For more details see Zychaluk and Foster (2009) <doi:10.3758/APP.71.6.1414> and Foster and Zychaluk (2007) <doi:10.1109/MSP.2007.4286564>.