  While recent advancements in Neural Ranking Models have resulted in
significant improvements over traditional statistical retrieval models, it is
generally acknowledged that the use of large neural architectures and the
application of complex language models in Information Retrieval (IR) have
reduced the transparency of retrieval methods. Consequently, Explainability and
Interpretability have emerged as important research topics in IR. Several
axiomatic and post-hoc explanation methods, as well as approaches that attempt
to be interpretable-by-design, have been proposed. This article presents
\irexplain, an open-source Python library that implements a variety of
well-known techniques for Explainable IR (ExIR) within a common, extensible
framework. \irexplain supports the three standard categories of post-hoc
explanations, namely pointwise, pairwise, and listwise explanations. The
library is designed to make it easy to reproduce state-of-the-art ExIR
baselines on standard test collections, as well as to explore new approaches to
explaining IR models and methods. To facilitate adoption, \irexplain is
well-integrated with widely-used toolkits such as Pyserini and \irdatasets.
