  We develop a new class of spatial voting models for binary preference data
that can accommodate both monotonic and non-monotonic response functions, and
are more flexible than alternative "unfolding" models previously introduced in
the literature. We then use these models to estimate revealed preferences for
legislators in the U.S. House of Representatives and justices on the U.S.
Supreme Court. The results from these applications indicate that the new models
provide superior complexity-adjusted performance to various alternatives and
also that the additional flexibility leads to preferences' estimates that are
closer matches to the perceived ideological positions of legislators and
justices.
