On the opposite end of the spectrum, the open source community has developed robust software approaches to address the challenges associated with MS data analysis. In the programming language R, the main focus of this article, these include powerful, but low-level R packages such as mzR and the more modern MSnbase [9], , which offer great flexibility in analysis of MS data and can be used for a first-look data exploration and pre-screening, but require some programming expertise to fully utilize their functionality. On the other end of the scale, there are options such as xcms , RforMassSpectrometry and patRoon [13], which provide comprehensive solutions for nontargeted metabolomics and environmental workflows. These approaches are very powerful, but often require significant computational resources, extended configuration time, and expertise in navigating their complex functionality. Further extending the toolkit, MetFrag and RMassBank are both methods to support compound identification. MetFrag focuses on in silico fragmentation to match experimental data to candidate compounds, aiding in compound annotation during suspect and nontarget screening workflows . RMassBank facilitates the creation and management of high-quality spectral libraries, for higher confident compound annotation , but was originally designed to process single-injection pure standards and not mixtures. Despite their utility, these tools often serve specialized needs within broader HRMS workflows, and integrating them effectively can be challenging for users without extensive computational experience.