Change-in-ratio (CIR) methods estimate population abundance based on changes in the composition of populations before and after interventions, such as hunter harvest. Change-in-ratio methods offer a cost-effective and efficient alternative to population analysis because the data can be obtained easily. Despite a long history of use and several quantitative advancements, computational requirements have limited the interest and opportunity to use more informative and precise CIR methods. In this paper we illustrate the application of multi-dimensional CIR methods to estimate abundance using program USER (User Specified Estimation Routine), which constructs likelihood models based on multinomial or product multinomial sampling distributions. We discuss multi-class and sequential CIR methods and illustrate how Program USER can be used to calculate maximum likelihood estimates. Our hope is that statistical software such as Program USER promotes awareness and interest in using CIR methods for demographic assessments of wild populations. However, it is important to carefully consider the assumptions of CIR techniques prior to implementation.