tidyvpc: VPC Percentiles and Prediction Intervals

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-prerelease: tidyvpc_1.0.0.zip, r-release: tidyvpc_1.0.0.zip, r-oldrel: tidyvpc_1.0.0.zip
macOS binaries: r-prerelease: tidyvpc_1.0.0.tgz, r-release: tidyvpc_1.0.0.tgz, r-oldrel: not available


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