Package: lax 1.2.3

lax: Loglikelihood Adjustment for Extreme Value Models

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.

Authors:Paul J. Northrop [aut, cre, cph], Camellia Yin [aut, cph]

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NEWS

# Install 'lax' in R:
install.packages('lax', repos = c('https://paulnorthrop.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/paulnorthrop/lax/issues

Datasets:
  • ow - Oxford and Worthing annual maximum temperatures

On CRAN:

clustered-dataclusterscomposite-likelihoodevdextreme-value-analysisextreme-value-statisticsextremesindependence-loglikelihoodloglikelihood-adjustmentmlepotregressionregression-modellingrobustsandwichsandwich-estimator

9 exports 3 stars 1.27 score 54 dependencies 13 scripts 364 downloads

Last updated 7 months agofrom:19ce998fd4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 23 2024
R-4.5-winOKAug 23 2024
R-4.5-linuxOKAug 23 2024
R-4.4-winOKAug 23 2024
R-4.4-macOKAug 23 2024
R-4.3-winOKAug 23 2024
R-4.3-macOKAug 23 2024

Exports:alogLikfit_bernoulligev_refitgpd_refitlogLikVecpot_refitpp_refitreturn_levelrlarg_refit

Dependencies:abindbackportsbayesplotchandwichcheckmateclicolorspacedistributionaldplyrexdexfansifarvergenericsggplot2ggridgesgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigplyrposteriorR6RColorBrewerRcppRcppArmadilloRcppRollreshape2revdbayesrlangrustsandwichscalesstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithrzoo

An overview of lax

Rendered fromlax-vignette.Rmdusingknitr::rmarkdownon Aug 23 2024.

Last update: 2024-02-25
Started: 2019-08-21

Readme and manuals

Help Manual

Help pageTopics
lax: Loglikelihood Adjustment for Extreme Value Modelslax-package lax
Loglikelihood adjustment for model fitsalogLik
Comparison of nested modelsanova.lax
Inference for the Bernoulli distributionalogLik.bernoulli bernoulli coef.bernoulli fit_bernoulli logLik.bernoulli logLikVec.bernoulli nobs.bernoulli vcov.bernoulli
Loglikelihood adjustment for eva fitsalogLik.gevrFit alogLik.gpdFit eva
Loglikelihood adjustment for evd fitsalogLik.evd evd
Loglikelihood adjustment for evir fitsalogLik.gev alogLik.gpd alogLik.potd evir
Loglikelihood adjustment for extRemes fitsalogLik.fevd extRemes
Loglikelihood adjustment for fExtremes fitsalogLik.fGEVFIT alogLik.fGPDFIT fExtremes
Loglikelihood adjustment for ismev fitsalogLik.gev.fit alogLik.gpd.fit alogLik.pp.fit alogLik.rlarg.fit ismev
Maximum-likelihood (Re-)Fitting using the ismev packagegev_refit gpd_refit ismev_refits pp_refit rlarg_refit
Sum loglikelihood contributions from individual observationslogLik.logLikVec
Evaluate loglikelihood contributions from specific observationslogLikVec
Loglikelihood adjustment for mev fitsalogLik.mev_egp alogLik.mev_gev alogLik.mev_gpd alogLik.mev_pp alogLik.mev_rlarg mev
Oxford and Worthing annual maximum temperaturesow
Plot diagnostics for a retlev objectplot.retlev
Loglikelihood adjustment for POT fitsalogLik.uvpot POT
Fits a Poisson point process to the data, an approach sometimes known as peaks over thresholds (POT), and returns an object of class "potd".pot_refit
Print method for retlev objectprint.retlev
Print method for objects of class '"summary.retlev"'print.summary.retlev
Return Level Inferences for Stationary Extreme Value Modelsreturn_level
Summary method for a '"retlev"' objectsummary.retlev
Loglikelihood adjustment of texmex fitsalogLik.evmOpt texmex