Sniffing Emergence and Trajectories in Academic Papers and Patents // Provides a unified set of methods to detect scientific emergence and technological trajectories in academic papers and patents. The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) doi:10.1002/bbb.2441, Souza et al. (2022) doi:10.14211/ibjesb.e1742, Matos et al. (2023) doi:10.1007/s43938-023-00036-3, Maria et al. (2023) doi:10.3390/su15020967, Biazatti et al. (2024) doi:10.1016/j.envdev.2024.101074, Felizardo et al. (2025) doi:10.1007/s12649-025-03136-z, and Miranda et al. (2025) doi:10.1016/j.ijhydene.2025.01.089.