Static analyzers have been successfully developed to detect runtime errors in many languages. However, the automatic analysis of functional languages remains a challenge due to their recursive functions, recursive algebraic data types, and higher-order functions. Classic type systems provide compositional methods that are in general not precise enough to prove the absence of runtime errors such as assertion failures. At the other end of the spectrum, deductive methods are more expressive but may require user guidance to prove invariants. Our work describes a static value analysis by abstract interpretation for a higher-order pure functional language. This analysis provides a sound and automatic approach to discover invariants and prevent assertion and match failures. We have designed a compositional analysis: functions are analyzed only once, at their definition site, generating a summary of their behavior. The summaries can be viewed as input-output relations expressed with relational abstract domains. We present two new abstract domains. A first abstract domain summarizes recursive algebraic data types. A second abstract domain lifts existing disjunctive relational summaries to higher-order by formalizing them as domains able to abstract higher-order functions. Both abstractions are parameterized by the abstractions of basic types (strings, integers, . . . ). Thanks to this parametric nature, both domains can be combined, allowing the analysis of higher-order functions manipulating algebraic data types and, conversely, algebraic data types using functions as first-class values. We have implemented this analysis in the open-source MOPSA platform. Preliminary evaluation confirms the precision of our approach on a set of 40 handwritten toy programs as well as 20 programs from the state-of-the-art Salto analyzer benchmark.