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.

Statisticians often want to compare the fit of different models on 
    the same data set. However, this usually involves a lot of manual code to 
    fish items out of summary() or plain model objects. 'modelfactory' offers 
    the capability to pass multiple models in and get out metrics or 
    coefficients for quick comparison with easy-to-remember syntax.

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.

A minimalistic but flexible framework that facilitates the
    creation, management and training of multiple 'caret' models. A model grid
    consists of two components, (1) a set of settings that is shared by all
    models by default, and (2) specifications that apply only to the individual
    models. When the model grid is trained, model and training specifications are
    first consolidated from the shared and the model specific settings into
    complete 'caret' model configurations. These models are then trained with
    the 'train()' function from the 'caret' package.

Calculate the financial impact of using a churn model in terms of cost, revenue, profit and return on investment.

An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.