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-4.5)weightQuant_1.0.1.zip(r-4.4)weightQuant_1.0.1.zip(r-4.3)
weightQuant_1.0.1.tgz(r-4.4-any)weightQuant_1.0.1.tgz(r-4.3-any)
weightQuant_1.0.1.tar.gz(r-4.5-noble)weightQuant_1.0.1.tar.gz(r-4.4-noble)
weightQuant_1.0.1.tgz(r-4.4-emscripten)weightQuant_1.0.1.tgz(r-4.3-emscripten)
weightQuant.pdf |weightQuant.html✨
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 3 years agofrom:f8ae38813e. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 14 2024 |
R-4.5-win | NOTE | Nov 14 2024 |
R-4.5-linux | NOTE | Nov 14 2024 |
R-4.4-win | NOTE | Nov 14 2024 |
R-4.4-mac | NOTE | Nov 14 2024 |
R-4.3-win | OK | Nov 14 2024 |
R-4.3-mac | OK | Nov 14 2024 |
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 |