# Machine Learning and Inference for Topological Data Analysis

## Documentation for package ‘TDApplied’ version 2.0.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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