Methods for Clustering Mixed-Type Data R package kamila version 0.1.2 Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou (2018) <doi:10.18637/jss.v083.i13>. Software Depends: R (>= 3.0.0) Imports: stats, abind, KernSmooth, gtools, Rcpp, plyr LinkingTo: Rcpp Suggests: testthat, clustMD, ggplot2, Hmisc Alexander Foss <alexanderhfoss@gmail.com> Comprehensive R Archive Network (CRAN) Alexander Foss [aut, cre], Marianthi Markatou [aut] GPL-3 file LICENSE (https://CRAN.R-project.org/package=kamila/LICENSE) 2020-03-13 application/tgz https://CRAN.R-project.org/package=kamila doi:10.32614/CRAN.package.kamila