A lightweight toolkit to validate new observations when computing their predictions with a predictive model. The validation process consists of two steps: (1) record relevant statistics and meta data of the variables in the original training data for the predictive model and (2) use these data to run a set of basic validation tests on the new set of observations.
| Version: | 0.8.2 |
| Depends: | R (≥ 3.4.0) |
| Imports: | data.table, crayon |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2019-06-13 |
| DOI: | 10.32614/CRAN.package.recorder |
| Author: | Lars Kjeldgaard [aut, cre] |
| Maintainer: | Lars Kjeldgaard <lars_kjeldgaard at hotmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/smaakage85/recorder |
| NeedsCompilation: | no |
| CRAN checks: | recorder results |
| Reference manual: | recorder.pdf |
| Vignettes: |
Introduction to recorder |
| Package source: | recorder_0.8.2.tar.gz |
| Windows binaries: | r-devel: recorder_0.8.2.zip, r-release: recorder_0.8.2.zip, r-oldrel: recorder_0.8.2.zip |
| macOS binaries: | r-release (arm64): recorder_0.8.2.tgz, r-oldrel (arm64): recorder_0.8.2.tgz, r-release (x86_64): recorder_0.8.2.tgz, r-oldrel (x86_64): recorder_0.8.2.tgz |
| Old sources: | recorder archive |
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