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.
- Version1.5.2
- R version≥ 3.5.0
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- doi:10.1002/psp4.12319
- Last release11/21/2024
Documentation
Team
James Craig
MaintainerShow author detailsBill Denney
Samer Mouksassi
Show author detailsRolesAuthorBenjamin Rich
Show author detailsRolesAuthorMichael Tomashevskiy
Show author detailsRolesContributorCertara USA, Inc.
Show author detailsRolesCopyright holder, fndOlivier Barriere
Show author detailsRolesAuthorKris Jamsen
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 678 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 25 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 7,798 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Nov 23, 2024 with 64 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports10 packages
- Suggests11 packages
- Reverse Imports1 package
- Reverse Suggests2 packages