These days more and more data are being collected, analysed and interpreted to inform decisions. Here, statisticians have a responsibility to support users who are interpreting the of results of a statistical analysis. At the same time, increasingly complex analysis tools are being developed, and are now easily accessible to non-statisticians through R packages. We as developers of complex statistical methods have a related responsibility to support the adequate use our methods by often quantitatively trained, yet non-specialist, scientists. But are these truly accessible?

When introducing these users to our methodology we need to strike the right balance between treating methodology as a mere black box and explaining every single technical detail, while providing an adequate understanding of the methodology that allows users to independently decide on appropriate model choices. This is needed to encourage the use of our methods as well as to establish a fruitful dialogue with the users to improve and successfully build on exciting methods.

In addition, when we develop statistical modelling approaches, it is important to ensure that these are relevant to the users and that they take into account the specific needs of the user community. This involves exploring and engaging with the specific data structures and associated scientific questions typically addressed within a field. We will focus here on the context of ecology and discuss specific data structures and questions arising within that field.

In this talk we will briefly discuss the capabilities of the software inlabru, but we will put a strong emphasis on exploring the need for – as well as approaches to – communicating the methodology well to potential users. We use the example of the software package inlabru and the associated broad range of statistical methodology to outline an approach to addressing the issue of juggling the right balance between treating an approach as a black box and explaining the every single mathematical detail of a modelling approach. In particular, we will discuss our recent thoughts on and attempts to finding a level of explanation that summarises what the methodology does through focusing on the role of the different model components and how they are reflected in the syntax of the package.