A versatile R package for creating and pricing custom derivatives to suit your financial needs.

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.

A simple way to write ".Rprofile" code in an R Markdown file and 
    have it knit to the correct location for your operating system.

Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.

Calculate the distribution of costs for the installation of an elevator based on the different distribution rules.

Provides functions for constructing and evaluating
    CUSUM charts and RA-CUSUM charts with focus on false signal probability.

Create CUSUM (cumulative sum) statistics from a vector or dataframe.
    Also create single or faceted CUSUM control charts, with or without control limits.
    Accepts vector, dataframe, tibble or data.table inputs.

Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.