Estimation of Extreme Conditional Quantiles and Probabilities
Gilles Durrieu, Ion Grama, Kevin Jaunatre, Quang-Khoai Pham, Jean-Marie
Tricot
Extreme value theory, nonparametric kernel estimation, tail
conditional probabilities, extreme conditional quantile, adaptive estimation,
quantile regression, survival probabilities.

Detect extreme values in onedimensional data
Outlier detection in onedimensional data

Detect outliers in one-dimensional data.
gWidgets, gWidgetstcltk, utils, stats, graphics

gWidgets2, gWidgets2tcltk, utils, stats, graphics

Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).
c(person("Miguel", "de Carvalho", role = c("aut", "cre"),
email = "miguel.decarvalho@ed.ac.uk"),
person("Rodrigo", "Rubio", role = c("aut"),
email = "rerubio@mat.uc.cl"))
Miguel de Carvalho [aut, cre],
Rodrigo Rubio [aut]

c(person("Miguel", "de Carvalho", role = c("aut", "cre"),
email = "miguel.decarvalho@ed.ac.uk"),
person("Rodrigo", "Rubio", role = c("aut"),
email = "rerubio@mat.uc.cl"),person("Vianey", "Palacios", role = c("aut"),
email = "s1875879@ed.ac.uk"),person("Alejandro", "Jara", role = c("ctb"),
email = "atjara@uc.cl"),person("Tim", "Hanson", role = c("ctb"),
email = "hansont@stat.sc.edu"))
Miguel de Carvalho [aut, cre],
Rodrigo Rubio [aut],
Vianey Palacios [aut],
Alejandro Jara [ctb],
Tim Hanson [ctb]

Estimation of Extreme Value Dependence for Time Series Data
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.

Estimation of the sample univariate, cross and return time extremograms. The package can also add empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.