Given a large set of problems and their individual solutions case based reasoning seeks to solve a new problem by referring to the solution of that problem which is "most similar" to the new problem. Crucial in case based reasoning is the decision which problem "most closely" matches a given new problem. The basic idea is to define a family of distance functions and to use these distance functions as parameters of local averaging regression estimates of the final result. Then that distance function is chosen for which the resulting estimate is optimal with respect to a certain error measure used in regression estimation. The idea is based on: Dippon J. et al. (2002) <doi:10.1016/S0167-9473(02)00058-0>.

Version: | 0.1 |

Imports: | R6, ranger, survival, tidyverse, cowplot, dplyr, data.table, magrittr, rms, Rcpp, RcppParallel |

LinkingTo: | Rcpp, RcppArmadillo, RcppParallel |

Suggests: | testthat, knitr, rmarkdown, RcppArmadillo |

Published: | 2018-06-12 |

Author: | Dr. Simon Mueller, PD Dr. Juergen Dippon |

Maintainer: | Dr. Simon Mueller <simon.mueller at muon-stat.com> |

BugReports: | https://github.com/sipemu/case-based-reasoning/issues |

License: | AGPL |

NeedsCompilation: | yes |

SystemRequirements: | C++11 |

Materials: | README |

CRAN checks: | CaseBasedReasoning results |

Reference manual: | CaseBasedReasoning.pdf |

Vignettes: |
Case Based Reasoning: Cox-Beta-Model Motivation Case Based Reasoning: RandomForest-Model |

Package source: | CaseBasedReasoning_0.1.tar.gz |

Windows binaries: | r-devel: CaseBasedReasoning_0.1.zip, r-release: CaseBasedReasoning_0.1.zip, r-oldrel: CaseBasedReasoning_0.1.zip |

OS X binaries: | r-release: CaseBasedReasoning_0.1.tgz, r-oldrel: not available |

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