Ecological inference (EI) encompasses diverse methods to address the frequent lack of individual-level data in various areas, particularly in decision-making processes. Considering only aggregated information, these methods allow inferences to be drawn about individual behaviors and preferences. The difficulty in EI stems from the potential for the ecological fallacy, where inferences drawn from aggregated data do not accurately reflect relationships at the individual level. Numerous statistical techniques and computational tools have been developed to address this challenge. This study focuses on comparing and contrasting several prominent R packages specifically designed for ecological inference: lphom, eiPack, eiCircle, ecolRxC and eiopt2. The comparison delves into the distinct computational approaches employed by each package, examines the underlying statistical models that form their foundation, and critically evaluates how each handles the inherent uncertainty associated with ecological inference. This includes considering how each package addresses potential biases and limitations inherent in aggregated data. To provide a robust empirical assessment, this study leveraged paired survey data from the Spanish Center for Sociological Research (CIS) focusing on pre- and post-election studies related to the 2015 Spanish General Election.