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We introduce GWDALI, a new Fisher-matrix, python based software that computeslikelihood gradients to forecast parameter-estimation precision of arbitrarynetwork of terrestrial gravitational wave detectors observing compact binarycoalescences. The main new feature with respect to analogous software is toassess parameter uncertainties beyond Fisher-matrix approximation, using thederivative approximation for Likelihood (DALI). The software makes optional useof the LSC algorithm library LAL and the stochastic sampling algorithm Bilby,which can be used to perform Monte-Carlo sampling of exact or approximatelikelihood functions. As an example we show comparison of estimated precisionmeasurement of selected astrophysical parameters for both the actuallikelihood, and for a variety of its derivative approximations, which turn outparticularly useful when the Fisher matrix is not invertible.

GWDALI: A Fisher-matrix based software for gravitational wave parameter-estimation beyond Gaussian approximation