Group matching is a common challenge for researchers conducting observational studies. Unbalanced variables can confound results, skewing them toward variables like age and sex, or anything else irrelevant to the study’s purpose. While manual matching of groups is possible in simpler data sets, group matching algorithms have been developed to make group matching manageable and replicable, particularly when several confounding variables are present. Such algorithms are typically released as R packages, making familiarity with R a prerequisite for interfacing with the algorithms and modifying their behavior. Accessibility to matching algorithms is consequently limited to researchers who are not proficient in R. In this thesis project, the web application IterMatchApp was developed to increase the usability and functionality of an existing R package, iterMatch. IterMatchApp ensures that the iterMatch algorithm robustly tolerates a wide range of data sets, and places minimal restrictions on the incoming data files. The app implements a graphical user interface that integrates with iterMatch and handles fundamental operations like file IO, parameter configuration, and data cleaning. Quality of life features are included to offer researchers increased efficiency and control over the matching process. Features have been added to increase the interpretability of results, provide insight to the algorithm’s behavior, and allow meaningful monitoring of the algorithm during execution and after. Fundamentally, this project aims to offer support to a broad range of observational studies by simplifying the interface between researchers and this group matching algorithm.