c(person(given = "Cord", family = "Hockemeyer",
role = c("aut", "cre"),
email = "cord.hockemeyer@uni-graz.at"))
Knowledge space theory by Doignon and Falmagne (1999)
<doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
framework which proposes mathematical formalisms to operationalize
knowledge structures in a particular domain.  The 'kstIO' package
provides basic functionalities to read and write KST data from/to files
to be used together with the 'kst', 'pks' or 'DAKS' packages.

Knowledge space theory by Doignon and Falmagne (1999)
<doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
framework which proposes mathematical formalisms to operationalize
knowledge structures in a particular domain.  The 'kstIO' package
provides basic functionalities to read and write KST data from/to
files to be used together with the 'kst', 'kstMatrix', 'pks' or
'DAKS' packages.

R (>= 3.5.0), pks (>= 0.4-0), MASS, stringr, sets, relations

Basic Functions in Knowledge Space Theory Using Matrix
Representation
Knowledge space theory by Doignon and Falmagne (1999)
<doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
framework, which proposes mathematical formalisms to operationalize
knowledge structures in a particular domain. The 'kstMatrix' package
provides basic functionalities to generate, handle, and manipulate
knowledge structures and knowledge spaces. Opposed to the 'kst'
package, 'kstMatrix' uses matrix representations for knowledge
structures. Furthermore, 'kstMatrix' contains several knowledge spaces
developed by the research group around Cornelia Dowling through
querying experts.

c(person(given = "Cord", family = "Hockemeyer",
role = c("aut", "cre"),
email = "cord.hockemeyer@uni-graz.at"),
person(given = "Wai", family = "Wong",
role = c("aut"),
email = "wai.wong@kuleuven.be"))
Cord Hockemeyer [aut, cre],
Wai Wong [aut]

Juan Domingo Gonzalez [cre, aut],
Victor J. Yohai [aut],
Ruben H. Zamar [aut]
c(person("Juan Domingo", "Gonzalez", email = "juanrst@hotmail.com", role = c("cre","aut")),
person("Victor J.", "Yohai", email = "victoryohai@gmail.com", role = "aut"),
person("Ruben H.", "Zamar", email = "ruben@stat.ubc.ca", role = "aut")
)
A clustering algorithm similar to K-Means is implemented, it has two main advantages,
namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when
there are atypical values in the sample and (b) The estimator is efficient, roughly speaking,
if there are no outliers in the sample, results will be similar than those obtained by a classic algorithm (K-Means).
Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale.
(see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).
R (>= 2.10), MASS, methods, dplyr, dbscan, stats, GSE