person("Erle", "Holgersen", role = "aut"),
person("Syed", "Haider", role = c("aut", "cre"), email = "Syed.Haider@icr.ac.uk")

Title: Compressed Hierarchical Clustering Toolbox
Description: Routines for efficient cluster analysis of large scale data. The package implements the 'CHICKN'
clustering algorithm, which clusters the data into K groups by relying on the compressed data version.
The package provides functions for data compression, hierarchical clustering and result post processing.
The used data compression procedure is based on the sketching learning N. 'Keriven', N. 'Tremblay', Y. 'Traonmilin',
R. 'Gribonval' (2017) <arXiv:1606.02838v2> and 'Nystrom' kernel approximation S. Wang, A. 'Gittens',
M. W. 'Mahoney' (2019) <arXiv:1706.02803v4>. Hierarchical clustering relies on the 'Compressive' K means algorithm
N. 'Keriven', A. 'Bourrier', R. 'Gribonval', P. Perez (2017) <arXiv:1610.08738v4>.
Imports: Rcpp, bigstatsr (>= 1.2.3), RcppParallel, mvnfast, zipfR,
MASS, pracma, nloptr, foreach, doRNG, parallel, doParallel,
LinkingTo: Rcpp, RcppArmadillo, RcppParallel, bigstatsr (>= 1.2.3),

Title: 'Compressive' Hierarchical Kernel Clustering Toolbox
person("Romain", "Guibert", role = "aut", email = "romain.guibert@icloud.com"),
Description: Routines for efficient cluster analysis of large scale data. This package implements the 'CHICKN'
clustering algorithm (see 'Permiakova' 'et' 'al.' (2020) "'CHICKN': Extraction of 'peptide' 'chromatographic'
'elution' profiles from large scale mass 'spectrometry' data by means of 'Wasserstein' 'compressive' hierarchical
cluster analysis"). Functions for data compression, hierarchical clustering and post processing are provided.

person("Stef", "van Buuren", email = "stef.vanbuuren@tno.nl", role = c("cre", "aut")),
person("Iris", "Eekhout", email = "iris.eekhout@tno.nl", role = "aut"),
person("Marta", "Rubio Codina", email = "martarubio@iadb.org", role = "aut"),
person("Orazio", "Attanasio", email = "orazio.attanasio@yale.edu", role = "aut"),
person("Costas", "Meghir", email = "c.meghir@yale.edu", role = "aut"),
person("Emla", "Fitzsimons", email = "e.fitzsimons@ucl.ac.uk", role = "aut"),
person("Sally", "Grantham-McGregor", email = "sallymcgregor@yahoo.com", role = "aut"),
person("Maria Caridad", "Araujo", email = "mcaraujo@iadb.org", role = "aut"),
person("Susan", "Walker", email = "susan.walker@uwimona.edu.jm", role = "aut"),
person("Christine", "Powell", role = "aut"),
person("Ann", "Weber", email = "annweber@unr.edu", role = "aut"),
person("Lia", "Fernald", email = "fernald@berkeley.edu", role = "aut"),
person("Paul", "Verkerk", email = "paul.verkerk@tno.nl", role = "aut"),
person("Linda", "Richter", email = "linda.richter@wits.ac.za", role = "aut"),
person("Betsy", "Lozoff", email = "blozoff@umich.edu", role = "aut"))
Description: Measuring child development starts by collecting responses to
developmental milestones, such as "able to sit" or "says two words".
There are many ways to combine such responses into summaries.
The package bundles publicly available datasets with individual milestone
data for children aged 0-5 years, with the aim of supporting the construction,
evaluation, validation and interpretation of methodologies that aggregate
milestone data into informative measures of child development.