Clusters of Effect Curves in Quantile Regression Models
c(person("Gianluca", "Sottile", role = c("aut", "cre"),
email = "gianluca.sottile@unipa.it"),
person("Giada", "Adelfio", role = "ctb",
email = "giada.adelfio@unipa.it"))
Clustering method to cluster both curves effects, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2017) <https://iwsm2017.webhosting.rug.nl/IWSM_2017_V2.pdf>.
Gianluca Sottile [aut, cre],
Giada Adelfio [ctb]

c(person("Gianluca", "Sottile", role = c("aut", "cre"),
email = "gianluca.sottile@unipa.it"),
person("Giada", "Adelfio", role = "aut",
email = "giada.adelfio@unipa.it"))
Gianluca Sottile [aut, cre],
Giada Adelfio [aut]

Clusters of Effects Curves in Quantile Regression Models
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2017) <https://iwsm2017.webhosting.rug.nl/IWSM_2017_V2.pdf>.

Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.

Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models.
tmvtnorm, mvtnorm, truncnorm, MASS, mclust, msm

c( person("Damien", "McParland", email="damien.mcparland@ucd.ie",
role=c("aut", "cre")), person("Isobel Claire", "Gormley",
email="claire.gormley@ucd.ie", role=c("aut")))
Model-based clustering of mixed data (i.e. data which consist of
continuous, binary, ordinal or nominal variables) using a parsimonious
mixture of latent Gaussian variable models.
ggplot2, mclust, reshape2, MASS, msm, mvtnorm, parallel,
truncnorm, viridis, stats
Damien McParland [aut, cre],
Isobel Claire Gormley [aut]

k-Prototypes Clustering for Mixed Variable-Type Data
Functions to perform k-prototypes partitioning clustering for
mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>.