mlbench, caret, knitr, modeldata, dplyr (>= 0.7.4), rlang,
tidyrules

mlbench, knitr, modeldata, dplyr (>= 0.7.4), rlang,
tidyrules, rmarkdown

Evaluation Metrics for Customer Scoring Models Depending on
Binary Classifiers
Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.

Costs Allocation for the Installation of an Elevator
Calculate the distribution of costs for the installation of an elevator based on the different distribution rules.

Optimal Equal-HR Method to Find Two Cutpoints for U-Shaped
Relationships in Cox Model
Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.

L1-version of Cramer-von Mises Two Sample Tests
The package contains two functions for L1-version of
Cramer-von Mises Tests: one computes Cramer-von Mises
two sample test scores, the other cmoputes the exact p-value(s) for given
Cramer-von Mises two-sample test score(s) under the assumption that
the populations under comparison have the same probability distribution.
The L1-version Cramer-von Mises test, like its L2 counterpart,
is distribution-free, but of less computational intensity. In certain cases,
this version of Cramer-von Mises test is almost as powerful as its L2 counterpart.
Simulation study also shows that it is more powerful than the Kolmogorov-Smirnov
test in certain cases.