Measures of Representational Similarity Across Models
Canonical Correlation Analysis
Centered Kernel Alignment
Dot product similarity
Hilbert Schmidt Independence Criterion
Linear regression fit similarity
Projection-Weighted Canonical Correlation Analysis
List of HSIC estimators
List of kernel functions
Singular Vector Canonical Correlation Analysis
Provides a collection of methods for quantifying representational similarity between learned features or multivariate data. The package offers an efficient 'C++' backend, designed for applications in machine learning, computational neuroscience, and multivariate statistics. See Klabunde et al. (2025) <doi:10.1145/3728458> for a comprehensive overview of the topic.