Robust environmental exposure pattern recognition and outlier detection with the open-source R package pcpr.

Background: Pattern recognition in high-dimensional mixture data is of increasing interest in environmental health (EH), as researchers often aim to identify sources or behaviors leading to potentially harmful exposures. Principal component pursuit (PCP)-a robust dimensionality reduction technique-has been successfully utilized for pattern recognition in a number of EH studies. PCP decomposes an exposure matrix into a low-rank matrix encoding consistent exposure patterns and a sparse matrix isolating outlying exposure events. However, PCP's application has been hindered by a lack of available software tailored specifically to EH research. Methods: We introduce an open-source R package, pcpr, enabling easy PCP deployment in EH research. The package provides functions to fit and fine-tune PCP models with three EH-specific extensions: (1) a non-negativity constraint on the low-rank matrix, enhancing interpretability; (2) procedures to accommodate missingness; and (3) a specialized penalty for observations below the analytic limit of detection. We illustrate core functionality by conducting a fully reproducible source-apportionment analysis of an air pollution mixture of 26 PM Results: The functions in pcpr autonomously extracted four consistent exposure patterns (secondary signal and tailpipe emissions, traffic, crustal dust, and salt) and 211 outlying exposure events (including July Fourth firework-related spikes) from the Queens PM Conclusion: The pcpr package facilitates robust, reproducible, and accessible exposure pattern recognition tailored to environmental epidemiology.