Dynamic dataflow models of computation have become widely used through their adoption to popular programming frameworks such as TensorFlow and GNU Radio. Although dynamic dataflow models offer more programming freedom, they lack analyzability compared to their static counterparts (such as synchronous dataflow). In this paper we advocate the use of a boundedly dynamic dataflow model of computation, VR-PRUNE, that remains analyzable but still offers more programming freedom than a fully static dataflow model. The paper presents the VR-PRUNE model of computation and runtime, and illustrates its applicability to practical signal processing applications by two use cases: an adaptive convolutional neural network, and a predistortion filter for wireless communications. By runtime experiments on two heterogeneous computing platforms we show that VR-PRUNE is both flexible and efficient.