% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TDApplied.R
\docType{package}
\name{TDApplied}
\alias{TDApplied}
\title{Machine learning and inference for persistence diagrams}
\description{
This package aims to bridge topological data analysis (TDA) with data, statistical
and machine learning practitioners so that more analyses may benefit from the
power of TDA. The main tool of TDA is persistent homology, which computes a 
shape descriptor of a dataset, called a persistence diagram. There are five
goals of this package: (1) convert the output from the persistent homology
calculations in the two main R package for TDA into a data frame which can
easily be used in personalized analyses, (2) provide a fast method for computing
distances between persistence diagrams, (3) implement kernel machine learning and statistical
methods for analyzing groups of persistence diagrams, (4) supply a fast persistent homology 
calculation via python and reticulate, and (5) provide tools for interpreting persistence
diagrams.
}
