Biology has entered the age of Big Data. The technical revolution has transformed the field,
and extracting meaningful information from large biological data sets is now a central …

Several persistent homology software libraries have been implemented in R. Specifically,
the Dionysus, GUDHI, and Ripser libraries have been wrapped by the TDA and TDAstats …

In this review, the state-of-the-art for goodness-of-fit testing for spatial point processes is
summarized. Test statistics based on classical functional summary statistics and recent …

Time-varying graphs are increasingly common in financial, social and biological data
analysis applications. Feature extraction that efficiently encodes the complex structure of …

In this work, we develop a method for detecting differences in the topological distribution of
cells forming epithelial tissues. In particular, we extract topological information from their …

In recent years, persistent homology has become an attractive method for data analysis. It
captures topological features, such as connected components, holes, and voids, from point …

Typological databases in linguistics are usually categorical-valued. As a result, it is difficult
to have a clear visualization of the data. In this paper, we describe a workflow to analyze the …

Over the past decade, the techniques of topological data analysis (TDA) have grown into
prominence to describe the shape of data. In recent years, there has been increasing …

Persistent homology is a widely-used tool in topological data analysis (TDA) for
understanding the underlying shape of complex data. By constructing a filtration of simplicial …

Abstract Topological Data Analysis (TDA) characterizes data based on topological invariants
present in the data. In general, TDA treats the data as a discrete sampling of an underlying …