  Over the past decade, contextual bandit algorithms have been gaining in
popularity due to their effectiveness and flexibility in solving sequential
decision problems---from online advertising and finance to clinical trial
design and personalized medicine. At the same time, there are, as of yet,
surprisingly few options that enable researchers and practitioners to simulate
and compare the wealth of new and existing bandit algorithms in a standardized
way. To help close this gap between analytical research and empirical
evaluation the current paper introduces the object-oriented R package
"contextual": a user-friendly and, through its object-oriented structure,
easily extensible framework that facilitates parallelized comparison of
contextual and context-free bandit policies through both simulation and offline
analysis.
