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Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
| Version: | 1.0.1 |
| Depends: | pch, survival, matrixStats, methods, utils |
| Published: | 2024-01-23 |
| DOI: | 10.32614/CRAN.package.Qest |
| Author: | Gianluca Sottile [aut, cre], Paolo Frumento [aut] |
| Maintainer: | Gianluca Sottile <gianluca.sottile at unipa.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.sciencedirect.com/science/article/abs/pii/S0167947322000512 |
| NeedsCompilation: | yes |
| CRAN checks: | Qest results |
| Reference manual: | Qest.pdf |
| Package source: | Qest_1.0.1.tar.gz |
| Windows binaries: | r-devel: Qest_1.0.1.zip, r-release: Qest_1.0.1.zip, r-oldrel: Qest_1.0.1.zip |
| macOS binaries: | r-release (arm64): Qest_1.0.1.tgz, r-oldrel (arm64): Qest_1.0.1.tgz, r-release (x86_64): Qest_1.0.1.tgz, r-oldrel (x86_64): Qest_1.0.1.tgz |
| Old sources: | Qest archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.