recorder: Toolkit to Validate New Data for a Predictive Model

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
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

Downloads:

Reference manual: recorder.pdf
Vignettes: Introduction to recorder
Package source: recorder_0.8.2.tar.gz
Windows binaries: r-devel: recorder_0.8.1.zip, r-release: recorder_0.8.1.zip, r-oldrel: recorder_0.8.2.zip
OS X binaries: r-release: recorder_0.8.2.tgz, r-oldrel: recorder_0.8.2.tgz
Old sources: recorder archive

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