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Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
| Version: | 0.1.1 |
| Imports: | Rcpp (≥ 0.11.5), maxLik, e1071, mgcv |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2019-12-16 |
| DOI: | 10.32614/CRAN.package.predkmeans |
| Author: | Joshua Keller |
| Maintainer: | Joshua Keller <joshua.keller at colostate.edu> |
| License: | GPL-3 | file LICENSE |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | predkmeans results |
| Reference manual: | predkmeans.pdf |
| Package source: | predkmeans_0.1.1.tar.gz |
| Windows binaries: | r-devel: predkmeans_0.1.1.zip, r-release: predkmeans_0.1.1.zip, r-oldrel: predkmeans_0.1.1.zip |
| macOS binaries: | r-release (arm64): predkmeans_0.1.1.tgz, r-oldrel (arm64): predkmeans_0.1.1.tgz, r-release (x86_64): predkmeans_0.1.1.tgz, r-oldrel (x86_64): predkmeans_0.1.1.tgz |
| Old sources: | predkmeans archive |
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