Package: texmex 2.4.9

texmex: Statistical Modelling of Extreme Values

Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for threshold selection and to diagnose estimation convergence.

Authors:Harry Southworth [aut, cre], Janet E. Heffernan [aut], Paul D. Metcalfe [aut], Yiannis Papastathopoulos [ctb], Alec Stephenson [ctb], Stuart Coles [ctb]

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

Peer review:

Bug tracker:https://github.com/harrysouthworth/texmex/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • liver - Liver related laboratory data
  • nidd - Rain, wavesurge, portpirie and nidd datasets.
  • portpirie - Rain, wavesurge, portpirie and nidd datasets.
  • rain - Rain, wavesurge, portpirie and nidd datasets.
  • summer - Air pollution data, separately for summer and winter months
  • wavesurge - Rain, wavesurge, portpirie and nidd datasets.
  • winter - Air pollution data, separately for summer and winter months

On CRAN:

7.05 score 7 stars 1 packages 67 scripts 635 downloads 2 mentions 70 exports 30 dependencies

Last updated 9 months agofrom:abe95da3b3. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-win-x86_64OKNov 19 2024
R-4.5-linux-x86_64OKNov 19 2024
R-4.4-win-x86_64OKNov 19 2024
R-4.4-mac-x86_64OKNov 19 2024
R-4.4-mac-aarch64OKNov 19 2024
R-4.3-win-x86_64OKNov 19 2024
R-4.3-mac-x86_64OKNov 19 2024
R-4.3-mac-aarch64OKNov 19 2024

Exports:addExcessesbootExtremalIndexbootMCSbootmexcalcJointExceedanceCurvecgpdchicopulacvdeclustdegp3dgevdglodgpddgumbeledfegp3egp3RangeFitendPointevmevm.declusteredevm.defaultevmBootevmRealevmSimevmSimSetSeedextremalIndexextremalIndexRangeFitgeom_jointExcCurvegevggplotrlglogpdgpd.profgpdIntCensoredgpdRangeFitgumbelJointExceedanceCurvelinearPredictorsmakeReferenceMarginalDistributionMCSmexmexAllmexDependencemexMonteCarlomexRangeFitmigpdmigpdCoefsmrlpegp3pgevpglopgpdpgumbelqegp3qgevqgloqgpdqgumbelregp3rFrechetrgevrglorgpdrgumbelrlrMaxARtexmexFamilythinAndBurnweibull

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepillarpkgconfigR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr

Vignettes for texmex package

Harry Southworth and Janet E. Heffernan

Rendered fromtexmexVignettes.Rmdusingknitr::rmarkdownon Nov 19 2024.

Last update: 2020-04-02
Started: 2020-04-02

Readme and manuals

Help Manual

Help pageTopics
Extreme value modellingtexmex-package texmex
Accurately compute (exp(x) - 1) / x.exprel
Accurately compute log(1-exp(x)).log1mexp
Accurately compute log(1 + x) / x.log1prel
Compute pmax(x y, -1) in such a way that zeros in x beat infinities in y..specfun.safe.product
Annotate a threshold selection ggplotaddExcesses
Information CriteriaAIC.evmOpt AIC.evmSim
Bootstrap a conditional multivariate extreme values modelbootmex plot.bootmex print.bootmex
Measures of extremal dependencechi ggplot.chi plot.chi print.chi print.summary.chi summary.chi
Calculate the copula of a matrix of variablescopula copula.data.frame copula.default copula.matrix
Cross-validation for a model objectcv ggplot.cv plot.cv print.cv summary.cv
Cross-validation for the shape parameter in an extreme values modelcv.evmOpt
Density, cumulative density, quantiles and random number generation for the extended generalized Pareto distribution 3degp3 pegp3 qegp3 regp3
Density, cumulative density, quantiles and random number generation for the generalized extreme value distributiondgev pgev qgev rgev
Density, cumulative density, quantiles and random number generation for the generalized Pareto distributiondgpd pgpd qgpd rgpd
The Gumbel distributiondgumbel pgumbel qgumbel rgumbel
Compute empirical distribution functionedf
Estimate the EGP3 distribution power parameter over a range of thresholdsegp3RangeFit ggplot.egp3RangeFit plot.egp3RangeFit print.egp3RangeFit
Calculate upper end point for a fitted extreme value modelendPoint endPoint.evmBoot endPoint.evmOpt endPoint.evmSim
Extreme value modellingevm evm.default evmReal
Bootstrap an evmOpt fitcoef.evmBoot evmBoot plot.evmBoot print.evmBoot print.summary.evmBoot summary.evmBoot
MCMC simulation around an evmOpt fitevmSim
Set the seed from a fitted evmSim object.evmSimSetSeed
Extremal index estimation and automatic declusteringbootExtremalIndex declust declust.default declust.extremalIndex evm.declustered extremalIndex extremalIndexRangeFit ggplot.extremalIndexRangeFit plot.declustered plot.extremalIndexRangeFit print.declustered print.extremalIndex
Fancy plotting for copulasggplot.copula
Diagnostic plots for an declustered objectggplot.declustered ggplot.extremalIndex
Diagnostic plots for the replicate estimated parameter values in an evmBoot objectggbootdensplots ggplot.evmBoot
Diagnostic plots for an evm objectggplot.evmOpt ggplot.evmOpt, ggplot.hist.evmOpt ggplot.ppevm ggplot.qqevm ggplotrl
Diagnostic plots for the Markov chains in an evmSim objectggacfplots ggdensplots ggplot.evmSim ggtraceplots
Conditional multivariate extreme values modellingggplot.mex ggplot.predict.mex mex mexAll plot.mex plot.predict.mex predict.mex print.mex print.mexList print.summary.mex summary.mex summary.predict.mex
Fit multiple independent generalized Pareto modelsggplot.migpd migpd plot.migpd
Plotting function for return level estimationggplot.lp.evmBoot ggplot.lp.evmOpt ggplot.lp.evmSim ggplot.rl.evmBoot ggplot.rl.evmOpt ggplot.rl.evmSim
Profile likelihood based confidence intervals for GPDgpd.prof
Estimate generalized Pareto distribution parameters over a range of valuesggplot.gpdRangeFit gpdRangeFit plot.gpdRangeFit print.gpdRangeFit print.summary.gpdRangeFit summary.gpdRangeFit
Joint exceedance curvescalcJointExceedanceCurve geom_jointExcCurve JointExceedanceCurve JointExceedanceCurve.default JointExceedanceCurve.mexMC JointExceedanceCurve.predict.mex print.jointExcCurve
Liver related laboratory dataliver
Log-likelihood for evmOpt objectslogLik.evmOpt
Provide full marginal reference distribution for for maringal transformationmakeReferenceMarginalDistribution
Multivariate conditional Spearman's rhobootMCS ggplot.bootMCS ggplot.MCS MCS plot.bootMCS plot.MCS print.bootMCS print.MCS print.summary.bootMCS summary.bootMCS
Estimate the dependence parameters in a conditional multivariate extreme values modelmexDependence
Simulation from dependence modelsmexMonteCarlo
Estimate dependence parameters in a conditional multivariate extreme values model over a range of thresholds.mexRangeFit
Change values of parameters in a migpd objectmigpdCoefs
Mean residual life plotggplot.mrl mrl plot.mrl print.mrl print.summary.mrl summary.mrl
Plot copulasplot.copula
Plots for evmOpt objectsplot.evmOpt
Plots for evmSim objectsplot.evmSim
Predict return levels from extreme value models, or obtain the linear predictors.linearPredictors linearPredictors.evmBoot linearPredictors.evmOpt linearPredictors.evmSim plot.lp.evmOpt predict.evmBoot predict.evmOpt predict.evmSim print.lp.evmOpt
Return levelsplot.rl.evmBoot plot.rl.evmOpt plot.rl.evmSim print.rl.evmOpt rl rl.evmBoot rl.evmOpt rl.evmSim
Print evmOpt objectsprint.evmOpt
Rain, wavesurge, portpirie and nidd datasets.nidd portpirie rain rain, wavesurge and portpirie wavesurge
Extreme Value random process generation.rFrechet
Generalized logistic distributiondglo pglo qglo rglo
Extreme Value random process generation.rMaxAR
Simulate from a fitted evm objectsimulate.evmBoot simulate.evmOpt simulate.evmSim
Air pollution data, separately for summer and winter monthssummer summer and winter data winter
Create families of distributionscgpd egp3 gev glo gpd gpdIntCensored gumbel print.summary.texmexFamily print.texmexFamily summary.texmexFamily texmexFamily weibull
Process Metropolis output from extreme value model fitting to discard unwanted observations.thinAndBurn thinAndBurn.evmSim