  The credit scoring industry has a long tradition of using statistical tools
for loan default probability prediction and domain specific standards have been
established long before the hype of machine learning. Although several
commercial software companies offer specific solutions for credit scorecard
modelling in R explicit packages for this purpose have been missing long time.
In the recent years this has changed and several packages have been developed
which are dedicated to credit scoring. The aim of this paper is to give a
structured overview on these packages. This may guide users to select the
appropriate functions for a desired purpose and further hopefully will
contribute to directing future development activities. The paper is guided by
the chain of subsequent modelling steps as they are forming the typical
scorecard development process.
