Research increasingly supports the interaction between cognitive and motor processes, yet traditional methods for analyzing postural control data often aggregate data over extended periods, making it difficult to capture the effects of individual cognitive events. Our recently developed R package forceplate offers a novel, event-related approach to analyze how cognitive tasks impact postural control at finer temporal scales using force-plate data. The forceplate package processes continuous force and moment data from the force plate across three spatial dimensions, segmenting these time series into trials aligned with discrete cognitive events. Users can apply a low-pass filter to mitigate artifacts such as muscle twitches or electrical noise and perform baseline corrections to standardize data across trials. With the package, users can define specific time bins around events (e.g., stimulus presentation or response onset) to calculate descriptive statistics like mean, and standard deviation for each trial’s time-series data. The output is a structured dataset with one or more measures per trial, depending on the number of user-defined time bins, that is readily compatible with further analyses, such as (mixed-effects) analysis of variance. By standardizing and simplifying the preprocessing of force-plate data for cognitive research, forceplate aims to improve reproducibility and comparability across studies. This R package thus provides a comprehensive tool for researchers seeking to explore cognitive-motor interactions on smaller time scales, contributing to standardized methodologies in the field.