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Creation of imprecise classification trees. They rely on probability estimation within each node by means of either the imprecise Dirichlet model or the nonparametric predictive inference approach. The splitting variable is selected by the strategy presented in Fink and Crossman (2013) <http://www.sipta.org/isipta13/index.php?id=paper&paper=014.html>, but also the original imprecise information gain of Abellan and Moral (2003) <doi:10.1002/int.10143> is covered.
| Version: | 0.5.1 |
| Imports: | Rcpp (≥ 0.12.5) |
| LinkingTo: | Rcpp |
| Suggests: | testthat |
| Published: | 2018-08-17 |
| DOI: | 10.32614/CRAN.package.imptree |
| Author: | Paul Fink [aut, cre] |
| Maintainer: | Paul Fink <paul.fink at stat.uni-muenchen.de> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Materials: | ChangeLog |
| CRAN checks: | imptree results |
| Reference manual: | imptree.pdf |
| Package source: | imptree_0.5.1.tar.gz |
| Windows binaries: | r-devel: imptree_0.5.1.zip, r-release: imptree_0.5.1.zip, r-oldrel: imptree_0.5.1.zip |
| macOS binaries: | r-release (arm64): imptree_0.5.1.tgz, r-oldrel (arm64): imptree_0.5.1.tgz, r-release (x86_64): imptree_0.5.1.tgz, r-oldrel (x86_64): imptree_0.5.1.tgz |
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