Package: weightQuant 1.0.1
weightQuant: Weights for Incomplete Longitudinal Data and Quantile Regression
Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.
Authors:
weightQuant_1.0.1.tar.gz
weightQuant_1.0.1.zip(r-4.7)weightQuant_1.0.1.zip(r-4.6)weightQuant_1.0.1.zip(r-4.5)
weightQuant_1.0.1.tgz(r-4.6-any)weightQuant_1.0.1.tgz(r-4.5-any)
weightQuant_1.0.1.tar.gz(r-4.7-any)weightQuant_1.0.1.tar.gz(r-4.6-any)
weightQuant_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
weightQuant/json (API)
| # Install 'weightQuant' in R: |
| install.packages('weightQuant', repos = c('https://vivianephilipps.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vivianephilipps/weightquant/issues
- simdata - Simulated dataset
Last updated from:f8ae38813e. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 135 | ||
| source / vignettes | OK | 170 | ||
| linux-release-x86_64 | NOTE | 130 | ||
| macos-release-arm64 | NOTE | 121 | ||
| macos-oldrel-arm64 | NOTE | 100 | ||
| windows-devel | NOTE | 126 | ||
| windows-release | NOTE | 86 | ||
| windows-oldrel | NOTE | 91 | ||
| wasm-release | OK | 97 |
Exports:bootwrqsummary.bootwrqtest.bootwrqweightsIMDweightsMMD
Dependencies:clicodetoolsdoParallelforeachglueiteratorslatticelifecyclemagrittrMASSMatrixMatrixModelsquantregrlangSparseMstringistringrsurvivalvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Weights for incomplete longitudinal data and quantile regression | weightQuant-package weightQuant |
| Bootstrap procedure for weighted quantile regressions | bootwrq |
| Simulated dataset | simdata |
| Summary of a quantile regression model | summary.bootwrq |
| Test of covariate effects between different quantiles | test.bootwrq |
| Estimation of observation-specific weights with intermittent missing data | weightsIMD |
| Estimation of observation-specific weights with monotone missing data | weightsMMD |
