SystemRequirements: C++14, gmp, GNU make

Title: Toolkit for Topological Data Analysis
Authors@R: c(person("Kisung", "You", role = c("aut", "cre"),email = "kyoustat@gmail.com",comment=c(ORCID="0000-0002-8584-459X")),person("Byeongsu","Yu", role="aut",email="byeongsu.yu@math.tamu.edu"))
Description: Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
Imports: Rcpp, Rdpack, TDAstats, energy, ggplot2, maotai, stats, utils

Authors@R: c(person("Kisung", "You", role = c("aut", "cre"),email = "kisungyou@outlook.com",comment=c(ORCID="0000-0002-8584-459X")),person("Byeongsu","Yu", role="aut",email="byeongsu.yu@math.tamu.edu"))
Imports: Rcpp, Rdpack, TDAstats, T4cluster, energy, ggplot2, maotai,

Title: Analyze High-Dimensional Data Using Discrete Morse Theory
Authors@R: c(person("Paul", "Pearson", email = "pearsonp@hope.edu",
role = c("aut", "cre","trl")), person("Daniel", "Muellner",
role = c("aut","ctb")), person("Gurjeet","Singh", role = c("aut","ctb")))
Description: Topological Data Analysis using Mapper (discrete Morse theory).
Generate a 1-dimensional simplicial complex from a filter
function defined on the data:  1. Define a filter function (lens) on the
data.  2. Perform clustering within within each level set and generate
one node (vertex) for each cluster.  3. For each pair of clusters in
adjacent level sets with a nonempty intersection, generate one edge
between vertices.  The function mapper1D uses a filter function with
codomain R, while the the function mapper2D uses a filter function with

Title: Machine Learning and Inference for Topological Data Analysis
Authors@R: c(person("Shael", "Brown", email = "shaelebrown@gmail.com", role = c("aut","cre")),
person("Dr. Reza", "Farivar", email = "reza.farivar@mcgill.ca", role = c("aut","fnd")))
Description: Topological data analysis is a powerful tool for finding non-linear global structure
in whole datasets. 'TDApplied' aims to bridge topological data analysis with data, statistical
and machine learning practitioners so that more analyses may benefit from the
power of topological data analysis. The main tool of topological data analysis is
persistent homology, which computes a shape descriptor of a dataset, called
a persistence diagram. There are three goals of this package: (1) convert persistence diagrams
computed using the two main R packages for topological data analysis into a data frame,
(2) implement fast versions of both distance and kernel calculations
for pairs of persistence diagrams, and (3) provide scalable methods for machine learning
Imports: parallel, doParallel, foreach, clue, rdist, parallelly,
kernlab, iterators, methods, stats, utils
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TDA, TDAstats
Packaged: 2022-08-12 15:04:40 UTC; jibaccount