  We introduce a Bayesian prior distribution, the Logit-Normal continuous
analogue of the spike-and-slab (LN-CASS), which enables flexible parameter
estimation and variable/model selection in a variety of settings. We
demonstrate its use and efficacy in three case studies -- a simulation study
and two studies on real biological data from the fields of metabolomics and
genomics. The prior allows the use of classical statistical models, which are
easily interpretable and well-known to applied scientists, but performs
comparably to common machine learning methods in terms of generalisability to
previously unseen data.
