STOPGAP is an open-source package designed for subtomogram averaging in cryo-electron tomography (cryo-ET). It provides detailed descriptions of the image-processing algorithms used, aiming to serve as a technical resource for users and facilitate community-driven software development. The package addresses the limitations of traditional single-particle analysis (SPA) by leveraging cryo-ET data to identify and localize target molecules, determine high-resolution structures, and classify different conformational states. STOPGAP uses a real-space correlation-based approach, including constrained cross-correlation (CCC) and a missing-wedge model that accounts for anisotropic sampling, CTF modulations, and exposure filtering. The package includes algorithms for template matching, subtomogram alignment, and classification, with features such as noise-correlation and multi-reference alignment (MRA) to improve the accuracy and reproducibility of results. STOPGAP is implemented in MATLAB and can be run as a compiled executable or through a toolbox of MATLAB functions and scripts. The package is designed to handle complex biological specimens, such as viral particles, cellular sections, and whole cells, enabling the structural characterization of molecules in their near-native environments.STOPGAP is an open-source package designed for subtomogram averaging in cryo-electron tomography (cryo-ET). It provides detailed descriptions of the image-processing algorithms used, aiming to serve as a technical resource for users and facilitate community-driven software development. The package addresses the limitations of traditional single-particle analysis (SPA) by leveraging cryo-ET data to identify and localize target molecules, determine high-resolution structures, and classify different conformational states. STOPGAP uses a real-space correlation-based approach, including constrained cross-correlation (CCC) and a missing-wedge model that accounts for anisotropic sampling, CTF modulations, and exposure filtering. The package includes algorithms for template matching, subtomogram alignment, and classification, with features such as noise-correlation and multi-reference alignment (MRA) to improve the accuracy and reproducibility of results. STOPGAP is implemented in MATLAB and can be run as a compiled executable or through a toolbox of MATLAB functions and scripts. The package is designed to handle complex biological specimens, such as viral particles, cellular sections, and whole cells, enabling the structural characterization of molecules in their near-native environments.