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) with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("tidyvpc")

1.0.0 by James Craig, 4 months ago


https://github.com/jameswcraig/tidyvpc


Report a bug at https://github.com/jameswcraig/tidyvpc/issues


Browse source code at https://github.com/cran/tidyvpc


Authors: Olivier Barriere [aut] , Benjamin Rich [aut] , James Craig [aut, cre] , Samer Mouksassi [aut] , Kris Jamsen [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports rlang, methods

Depends on data.table, magrittr, quantreg

Suggests cluster, classInt, KernSmooth, ggplot2, shiny, remotes, vpc, knitr, rmarkdown


See at CRAN