Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.
car, cluster, clValid, data.tree, dendextend, graphics,
jsonlite, mclust, methods, RCurl, reshape, reshape2, stringr,
utils

Analyze Clustered Data with Generalized Linear Models using the
Cluster Bootstrap
c(person(given = "Mathijs", family = "Deen",
role  = c("aut", "cre"),
email = "m.l.deen@fsw.leidenuniv.nl"),
person(given = "Mark", family = "de Rooij",
role = "aut"))
Provides functionality for the analysis of clustered data using the cluster bootstrap.
R (>= 3.0), stats, utils, graphics, parallel
Mathijs Deen [aut, cre],
Mark de Rooij [aut]

R (>= 3.0), stats, utils, graphics, parallel, magrittr, dplyr

Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans and
K-Medoids Clustering
Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids
clustering with the option to plot, validate, predict (new data) and estimate the
optimal number of clusters. The package takes advantage of 'RcppArmadillo' to
speed up the computationally intensive parts of the functions.
Rcpp (>= 0.12.5), OpenImageR, graphics, grDevices, utils, gmp,
FD, stats, ggplot2

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to
speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>.