An implementation of the TopoLow algorithm, a novel, physics-inspired method for antigenic cartography. TopoLow addresses significant challenges in mapping antigenic relationships, especially from sparse and noisy experimental data. The package transforms cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Mapping from Sparse Data: Effectively creates complete and consistent maps even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models antigens as particles connected by springs (for measured interactions) and subject to repulsive forces (for missing interactions), reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the antigenic map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation: Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various pathogens, including influenza, HIV, and Dengue viruses. It can be applied to any continuous and relational phenotype under directional selection pressure. Methods are described in Arhami and Rohani (2025) <doi:10.1093/bioinformatics/btaf372>.