This is a repository to reappear the paper gpinn

Hemorrhage classification on the basis of topological data analysis (persistent diagrams and persistent images) and machine learning techniques

From persistent diagrams compute a kernel similarity measure

Computation Phi_S Diagram

Persistence diagrams of proteins

Vietoris-Rips complex visualizer in canvas + D3

A parser for the output of the C++ library Ripser that calculates the Vietoris–Rips persistence barcodes. This makes it easier to be used with the TDA library in R.

R package porting Ripser-based persistent homology calculation engines from C++ via Rcpp. Currently ports Ripser (Vietoris-Rips complex) and Cubical Ripser (cubical complex).

High performance implementation of Vietoris-Rips persistence.

Ripser++: GPU-accelerated computation of Vietoris–Rips persistence barcodes

Ripser: efficient computation of Vietoris–Rips persistence barcodes

A full Bayesian framework is illustrated. The R-packages estimate the posteriors and classify any dataset converted to persistence diagrams.

Fully python + numba(jit) very basic algorithm to find homology groups and build persistence diagram from data.

R code associated with preprint proposing flat persistence diagram for better visualization of persistent homology.