The recent financial crisis has made clear to academics and regulators that it is not enough to assess systemic risks looking only at the health of individual institutions — due to interconnectedness, exposures that may seem harmless at the individual level may turn out to be systemically dangerous when taking into account the system as whole. Complex network theory and computer simulations can help one assess how risks could propagate in financial networks. Although an important subject, to the best of our knowledge the R community still lacks a package that implements systemic risk analysis tools for networks. The NetworkRiskMeasures package addresses this issue by providing a unified framework for analyzing risk in financial networks. It compiles several measures and algorithms used to estimate risk, both at the micro and macro levels, such as Default Cascades, DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity. In this presentation, we will first formally introduce some notions of financial risk measures and network theory. Then, using networks estimated by maximum entropy and minimum density methods, we illustrate how one can perform network risk assessment in practice using the NetworkRiskMeasures package.