1. The Q-estimator | 2. User-defined quantile functions | Censored data | 3. Families | 4. Robustness | 5. Proportional hazards models | 6. Numerical details | References
Package: Qest 2.0.0
Qest: Quantile-Based Estimators
Quantile-based estimators (Q-estimators) fit any parametric distribution through its quantile function, by minimizing the integrated loss of quantile regression. They are usually more robust than maximum likelihood estimators, and allow for censored and truncated data. The package implements user-defined quantile functions, built-in families (normal, gamma, continuous Poisson, uniform), linear models and proportional hazards models. The method is described in Sottile and Frumento (2022) <doi:10.1016/j.csda.2022.107471>.
Authors:
Qest_2.0.0.tar.gz
Qest_2.0.0.zip(r-4.7-any)Qest_2.0.0.zip(r-4.6-any)Qest_2.0.0.zip(r-4.5-any)
Qest_2.0.0.tgz(r-4.6-any)Qest_2.0.0.tgz(r-4.5-any)
Qest_2.0.0.tar.gz(r-4.7-any)Qest_2.0.0.tar.gz(r-4.6-any)
Qest_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Qest/json (API)
| # Install 'Qest' in R: |
| install.packages('Qest', repos = c('https://gianluca-sottile.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/gianluca-sottile/qest/issues
censored-datam-estimationproportional-hazardsquantile-functionquantile-regressionrobust-statisticsstatisticssurvival-analysistruncated-data
Last updated from:e1a601e25f. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 154 | ||
| source / vignettes | OK | 187 | ||
| linux-release | OK | 142 | ||
| macos-release | OK | 139 | ||
| macos-oldrel | OK | 149 | ||
| windows-devel | OK | 146 | ||
| windows-release | OK | 137 | ||
| windows-oldrel | OK | 107 | ||
| wasm-release | OK | 92 |
Exports:basehaz.QcoxphinvQQcoxphQcoxph.controlQestQest.controlQgammaQlmQlm.fitQnormQpoisQunifwtrunc
Dependencies:latticeMatrixmatrixStatssurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Baseline Cumulative Hazard of a Qcoxph Model | basehaz.Qcoxph |
| Inverse of a Quantile Function | invQ |
| Diagnostic Plot of Q-Estimators | plot.Qest |
| Predictions from a Qcoxph Model | predict.Qcoxph |
| Predictions from Q-Estimators | predict.Qest |
| Predictions from Qlm Models | predict.Qlm |
| Q-Estimation of Proportional Hazards Regression Models | Qcoxph |
| Quantile-Based Estimators (Q-Estimators) | Qest |
| Control Parameters of Qest, Qlm and Qcoxph | Qcoxph.control Qest.control |
| Family Objects for Qest | Qfamily Qgamma Qnorm Qpois Qunif |
| Q-Estimation of Linear Regression Models | Qlm Qlm.fit |
| Residuals of a Qcoxph Model | residuals.Qcoxph |
| Summarizing Qcoxph Fits | print.summary.Qcoxph summary.Qcoxph |
| Summarizing Q-Estimators | print.summary.Qest print.summary.Qlm summary.Qest summary.Qlm |
| Survival Curves from a Qcoxph Model | survfit.Qcoxph |
| Weights for Truncated Quantile Integration | wtrunc |