| bootstrap_persistence_thresholds | Estimate persistence threshold(s) for topological features in a data set using bootstrapping. |
| check_PyH_setup | Make sure that python has been configured correctly for persistent homology calculations. |
| check_ripser | Verify an imported ripser module. |
| diagram_distance | Calculate distance between a pair of persistence diagrams. |
| diagram_kernel | Calculate persistence Fisher kernel value between a pair of persistence diagrams. |
| diagram_kkmeans | Cluster a group of persistence diagrams using kernel k-means. |
| diagram_kpca | Calculate the kernel PCA embedding of a group of persistence diagrams. |
| diagram_ksvm | Fit a support vector machine model where each training set instance is a persistence diagram. |
| diagram_mds | Dimension reduction of a group of persistence diagrams via metric multidimensional scaling. |
| diagram_to_df | Convert a TDA/TDAstats persistence diagram to a data frame. |
| distance_matrix | Compute a distance matrix from a list of persistence diagrams. |
| gram_matrix | Compute the gram matrix for a group of persistence diagrams. |
| import_ripser | Import the python module ripser. |
| independence_test | Independence test for two groups of persistence diagrams. |
| permutation_test | Permutation test for finding group differences between persistence diagrams. |
| plot_diagram | Plot persistence diagrams |
| predict_diagram_kkmeans | Predict the cluster labels for new persistence diagrams using a pre-computed clustering. |
| predict_diagram_kpca | Project persistence diagrams into a low-dimensional space via a pre-computed kernel PCA embedding. |
| predict_diagram_ksvm | Predict the outcome labels for a list of persistence diagrams using a pre-trained diagram ksvm model. |
| PyH | Fast persistent homology calculations with python. |
| TDApplied | Machine learning and inference for persistence diagrams |