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TDA for Inference on Topological Data Analysis and Lessons from its Maintenance
We are pleased to have Jisu Kim, the maintainer of the R package TDA speaking to us about the package and his experience working with it! Title: R Package TDA for Statistical Inference on Topological Data Analysis and Lessons from its Maintenance Abstract: The first part of this presentation gives an introduction to the R package TDA, which provides some tools for Topological Data Analysis. The salient topological features of data can be quantified with persistent homology. The R package TDA provide an R interface for the efficient algorithms of the C++ libraries GUDHI, Dionysus, and PHAT. Specifically, The R package TDA includes functions for computing the persistent homology of the Rips complex, alpha complex, and alpha shape complex, and a function for the persistent homology of sublevel sets (or superlevel sets) of arbitrary functions evaluated over a grid of points or on data points. The R package TDA also provides a function for computing the confidence band that determines the significance of the features in the resulting persistence diagrams. The second part of this presentation is devoted to lessons I got from maintaining R package TDA, in particular about the memory access issues. When the R package is integrated with C++ code, the R package is exposed to possible memory issues such as memory leak due to the possibility of the direct access of memory in C++. Hence, CRAN is checking the possible memory access issues using address sanitizer, undefined behaviour sanitizer, etc. When your R package has such issues, the first step is to reproduce the issues in your environment. I will explain how to set up the environment for checking the memory access issues as CRAN does so that the issues are reproduced.

Carnegie Mellon (tentatively Baker Hall 235A)

4903 Frew Street · Pittsburgh, pa

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