Perform a Visual Predictive Check (VPC), while accounting for stratification, censoring, and prediction correction. Using piping from 'magrittr', the intuitive syntax gives users a flexible and powerful method to generate VPCs using both traditional binning and a new binless approach Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0), data.table (≥ 1.9.8), magrittr, quantreg (≥ 5.51) |
| Imports: | rlang (≥ 0.3.0), methods |
| Suggests: | cluster, classInt, KernSmooth, ggplot2, shiny, remotes, vpc, knitr, rmarkdown |
| Published: | 2020-03-26 |
| Author: | Olivier Barriere [aut], Benjamin Rich [aut], James Craig [aut, cre], Samer Mouksassi [aut], Kris Jamsen [ctb] |
| Maintainer: | James Craig <jameswbcraig at gmail.com> |
| BugReports: | https://github.com/jameswcraig/tidyvpc/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/jameswcraig/tidyvpc |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | tidyvpc results |
| Reference manual: | tidyvpc.pdf |
| Vignettes: |
Introduction to tidyvpc |
| Package source: | tidyvpc_1.0.0.tar.gz |
| Windows binaries: | r-devel: tidyvpc_1.0.0.zip, r-release: tidyvpc_1.0.0.zip, r-oldrel: tidyvpc_1.0.0.zip |
| macOS binaries: | r-release: tidyvpc_1.0.0.tgz, r-oldrel: tidyvpc_1.0.0.tgz |
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