Loglikelihood Adjustment for Extreme Value Models
Loglikelihood adjustment for model fits.
Comparison of nested models
Inference for the Bernoulli distribution
Loglikelihood adjustment for eva fits
Loglikelihood adjustment for evd fits
Loglikelihood adjustment for evir fits
Loglikelihood adjustment for extRemes fits
Loglikelihood adjustment for fExtremes fits
Maximum-likelihood (Re-)Fitting using the ismev package
Loglikelihood adjustment for ismev fits
Internal lax functions
lax: Loglikelihood Adjustment for Extreme Value Models
Sum loglikelihood contributions from individual observations
Evaluate loglikelihood contributions from specific observations
Loglikelihood adjustment for mev fits
Plot diagnostics for a retlev object
Fits a Poisson point process to the data, an approach sometimes known ...
Loglikelihood adjustment for POT fits
Print method for retlev object
Print method for objects of class "summary.retlev"
Return Level Inferences for Stationary Extreme Value Models
Summary method for a "retlev" object
Loglikelihood adjustment of texmex fits
Performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages 'eva' <https://cran.r-project.org/package=eva>, 'evd' <https://cran.r-project.org/package=evd>, 'evir' <https://cran.r-project.org/package=evir>, 'extRemes' <https://cran.r-project.org/package=extRemes>, 'fExtremes' <https://cran.r-project.org/package=fExtremes>, 'ismev' <https://cran.r-project.org/package=ismev>, 'mev' <https://cran.r-project.org/package=mev>, 'POT' <https://cran.r-project.org/package=POT> and 'texmex' <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the 'chandwich' package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the 'sandwich' package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.
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