Computation of the WHO 2007 References for School-Age Children
and Adolescents (5 to 19 Years)
c(person("Dirk", "Schumacher", ,
"mail@dirk-schumacher.net", c("aut", "cre")),
person("Elaine", "Borghi", , "borghie@who.int", "ctb"),
person("Giovanna", "Gatica-Domínguez", ,
"gaticag@who.int", "ctb"),
person(given = "World Health Organization", role = "cph"))
Provides WHO 2007 References for School-age Children and
Adolescents (5 to 19 years) (z-scores) with
confidence intervals and standard errors around the
prevalence estimates, taking into account complex sample designs.
More information on the methods is
available online:
<https://www.who.int/tools/growth-reference-data-for-5to19-years>.
Dirk Schumacher [aut, cre],
Elaine Borghi [ctb],
Giovanna Gatica-Domínguez [ctb],
World Health Organization [cph]

c(person(given = "Martin",
family = "Papenberg",
role = c("aut", "cre"),
email = "martin.papenberg@hhu.de",
comment = c(ORCID = "0000-0002-9900-4268")),
person(given = "Meik",
family = "Michalke",
role = "ctb",
comment = "centroid based clustering algorithm"),
person(given = c("Gunnar", "W."),
family = "Klau",
role = "ths"),
person(given = c("Juliane", "V."),
family = "Tkotz",
role = "ctb",
comment = "package logo"))
Martin Papenberg [aut, cre] (<https://orcid.org/0000-0002-9900-4268>),
Meik Michalke [ctb] (centroid based clustering algorithm),
Gunnar W. Klau [ths],
Juliane V. Tkotz [ctb] (package logo)
The method of anticlustering partitions a pool of
elements into groups (i.e., anticlusters) in such a way that the
between-group similarity is maximized and -- at the same time -- the
within-group heterogeneity is maximized. This reverses the logic of
cluster analysis that strives for high within-group homogeneity and
low similarity of the different groups. Computationally,
anticlustering is accomplished by maximizing instead of minimizing a
clustering objective function, such as the intra-cluster variance
(used in k-means clustering) or the sum of pairwise distances within
clusters.  The function anticlustering() implements exact and
heuristic anticlustering algorithms as described in Papenberg and Klau
(2020; <doi:10.1037/met0000301>). The exact approach requires that the
GNU linear programming kit
(<https://www.gnu.org/software/glpk/glpk.html>) is available and the R
package 'Rglpk' (<https://cran.R-project.org/package=Rglpk>) is
installed. Some other functions are available to solve classical
clustering problems. The function balanced_clustering() applies a
cluster analysis under size constraints, i.e., creates equal-sized
clusters. The function matching() can be used for (unrestricted,
bipartite, or K-partite) matching. The function wce() can be used
optimally solve the (weighted) cluster editing problem, also known as
correlation clustering, clique partitioning problem or transitivity
clustering.
The exact (anti)clustering algorithms require that
the GNU linear programming kit (GLPK library) is installed
(<http://www.gnu.org/software/glpk/>).