Imports: checkmate, httr, jsonlite, geojsonsf

Imports: checkmate, httr, janitor, jsonlite, geojsonsf

Imports: checkmate, httr, geojsonsf, janitor, jsonlite, sf

person("Marcus", "Young", email = "M.A.Young@soton.ac.uk", role = c("aut", "cre"),
Author: Marcus Young [aut, cre] (<https://orcid.org/0000-0003-4627-1116>)

Imports: checkmate, httr, geojsonsf, janitor, jsonlite, sf, urltools

Imports: checkmate, httr, geojsonsf, janitor, jsonlite, sf, urltools,

Description: Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2015) <https://arxiv.org/abs/1406.0808>, and Coretto and Hennig (2016) <https://arxiv.org/abs/1309.6895>.
Author: Pietro Coretto [aut, cre], Christian Hennig [aut]
Authors@R: c(person("Pietro", "Coretto", role = c("aut", "cre"), email = "pcoretto@unisa.it"),
person("Christian", "Hennig", role = "aut"))
Imports: stats, graphics, grDevices, mclust, prabclus

Description: Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <arXiv:1309.6895>.
Imports: stats, utils, graphics, grDevices, mclust, parallel, foreach,

Description: Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <http://jmlr.org/papers/v18/16-382.html>.