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Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.
| Version: | 0.1.0 |
| Published: | 2024-07-02 |
| DOI: | 10.32614/CRAN.package.ppRank |
| Author: | Tiago Costa Soares [aut, cre], Pedro Augusto Mendes [aut] |
| Maintainer: | Tiago Costa Soares <tiagocsoares22 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | ppRank results |
| Reference manual: | ppRank.pdf |
| Package source: | ppRank_0.1.0.tar.gz |
| Windows binaries: | r-devel: ppRank_0.1.0.zip, r-release: ppRank_0.1.0.zip, r-oldrel: ppRank_0.1.0.zip |
| macOS binaries: | r-release (arm64): ppRank_0.1.0.tgz, r-oldrel (arm64): ppRank_0.1.0.tgz, r-release (x86_64): ppRank_0.1.0.tgz, r-oldrel (x86_64): ppRank_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.