DESCRIPTION

Package: tidyvpc
Type: Package
Title: VPC Percentiles and Prediction Intervals
Version: 1.5.2
Authors@R: c(
      person("Olivier", "Barriere", email = "olivier.barriere@gmail.com",
      role = c("aut")),
      person("Benjamin", "Rich", email = "mail@benjaminrich.net",
      role = c("aut")),
      person("James", "Craig", email = "james.craig@certara.com",
      role = c("aut", "cre"),
      comment = c(ORCID = "0000-0003-1757-9234")),
      person("Samer", "Mouksassi", email = "samermouksassi@gmail.com",
      role = c("aut")),
      person("Bill", "Denney", email="wdenney@humanpredictions.com", 
      role="ctb", 
      comment=c(ORCID="0000-0002-5759-428X")),
      person("Michael", "Tomashevskiy", email = "michael.tomashevskiy@certara.com",
      role = c("ctb")),
      person("Kris", "Jamsen",
      role = c("ctb")),
      person("Certara USA, Inc.", role = c("cph","fnd"))
    )
Description: 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. 
URL: https://github.com/certara/tidyvpc
BugReports: https://github.com/certara/tidyvpc/issues
Depends:
    R (>= 3.5.0),
Imports:
    data.table (>= 1.9.8), 
    magrittr,
    quantreg (>= 5.51),
    rlang (>= 0.3.0), 
    methods,
    mgcv,
    classInt,
    cluster,
    ggplot2,
    stats,
    fastDummies,
    utils,
    egg
Suggests:
    dplyr,
    KernSmooth,
    knitr,
    R.rsp,
    nlmixr2,
    shiny,
    remotes,
    vpc,
    rmarkdown,
    testthat (>= 2.1.0),
    vdiffr (>= 1.0.0)
License: MIT + file LICENSE
LazyData: true
Encoding: UTF-8
VignetteBuilder: R.rsp
RoxygenNote: 7.3.2