This package was developed by Ciardo et.al. The model type used for input is a stochastic reward net (SRN). SRNs incorporate several structural extensions to GSPNs such as marking dependencies (marking dependent arc cardinalities, guards, etc.) and allow reward rates to be associated with each marking. The reward function can be marking dependent as well. They are specified using CSPL (C based SRN Language) which is an extension of the C programing language with additional constructs for describing the SRN models. SRN specifications are automatically converted into a Markov reward model which is then solved to compute a variety of transient, steady-state, cumulative, and sensitivity measures. For SRNs with absorbing markings, mean time to absorption and expected accumulated reward until absorption can be computed.