A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.
Version: 0.4.2 Depends: R (≥ 4.3.0) Imports: Rsolnp, nloptr, rugarch, zoo, xts Suggests: rmarkdown, testthat, PCRA Published: 2025-04-28 DOI: 10.32614/CRAN.package.robustGarch Author: Echo Liu [aut, cre], Daniel Xia [aut], R. Douglas Martin [aut] Maintainer: Echo Liu <yuhong.echo.liu at gmail.com> BugReports: https://github.com/EchoRLiu/robustGarch/issues License: MIT + file LICENSE URL: https://github.com/EchoRLiu/robustGarch NeedsCompilation: no Materials: README In views: TimeSeries CRAN checks: robustGarch results Documentation: Reference manual: robustGarch.pdf Downloads: Package source: robustGarch_0.4.2.tar.gz Windows binaries: r-devel: robustGarch_0.4.2.zip, r-release: robustGarch_0.4.2.zip, r-oldrel: robustGarch_0.4.2.zip macOS binaries: r-release (arm64): robustGarch_0.4.2.tgz, r-oldrel (arm64): robustGarch_0.4.2.tgz, r-release (x86_64): robustGarch_0.4.2.tgz, r-oldrel (x86_64): robustGarch_0.4.2.tgz Linking:Please use the canonical form https://CRAN.R-project.org/package=robustGarch to link to this page.
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