Python Representational Similarity Analysis toolbox (rsatoolbox)

The rsatoolbox is developed through a community effort (join us on github) by the labs of Nikolaus Kriegeskorte, Jörn Diedrichsen, Marieke Mur and Ian Charest. It was conceived during the RSA retreat 2019 in Blue Mountains, Ontario. The toolbox replaces the 2013 matlab version of rsatoolbox and reflects many of the new methodological developments.

Documentation

Contents:

• Toolbox overview
• Getting started
• Defining the data set
  • Temporal data sets
• Estimating dissimilarities
  • Euclidean Distance
  • Mahalanobis Distance
  • Crossnobis dissimilarity
  • Correlation Distance
  • Poisson Symmetrized KL-divergence
  • Cross-validated Poisson KL-divergence
• Operations on RDMs
  • Matrices and Vectors
  • Subset and Split
  • Concatenate
  • Missing data
  • Sort and reorder
  • Transformations
• Comparing RDMs
  • Cosine similarity
  • Pearson Correlation
  • Whitened comparison measures
  • Kendall’s tau
  • Spearman’s rho
  • Bures’s similarity
• Model Specification
  • Fixed models
  • Weighted models
  • Selection models
  • Interpolation models
  • Noise ceiling models
• Model Fitting
  • Individual vs. group fits
  • Continuous vs. rank-based evaluation metrics
  • Fitting algorithms
  • Calling the fitting function directly
  • Using the Fitter object
• Model Evaluation and Inference
  • Fixed evaluation and inference
  • Cross-validation
  • Bootstrapping
  • Bootstrap-cross-validation
  • Results objects
• Visualization
  • Plotting RDMs
  • Scatter plots
  • Ploting model evaluations
• References
• Demos
  • Basics
  • By Neuroimaging Modality
  • Miscellaneous
• rsatoolbox reference
  • rsatoolbox.data package
  • rsatoolbox.inference package
  • rsatoolbox.io package
  • rsatoolbox.model package
  • rsatoolbox.rdm package
  • rsatoolbox.simulation package
  • rsatoolbox.util package
  • rsatoolbox.vis package

Indices and tables

• Index

• Module Index

• Search Page