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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texmex_2.4.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
texmex/json (API)

# Install 'texmex' in R:

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

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:

cpp

6.91 score 7 stars 1 packages 82 scripts 2.4k downloads 2 mentions 70 exports 19 dependencies

Last updated from:abe95da3b3. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	218
linux-devel-x86_64	OK	168
source / vignettes	OK	223
linux-release-arm64	OK	209
linux-release-x86_64	OK	178
macos-release-arm64	OK	209
macos-release-x86_64	OK	464
macos-oldrel-arm64	OK	188
macos-oldrel-x86_64	OK	357
windows-devel	OK	135
windows-release	OK	144
windows-oldrel	OK	146
wasm-release	OK	130

Exports:addExcesses bootExtremalIndex bootMCS bootmex calcJointExceedanceCurve cgpd chi copula cv declust degp3 dgev dglo dgpd dgumbel edf egp3 egp3RangeFit endPoint evm evm.declustered evm.default evmBoot evmReal evmSim evmSimSetSeed extremalIndex extremalIndexRangeFit geom_jointExcCurve gev ggplotrl glo gpd gpd.prof gpdIntCensored gpdRangeFit gumbel JointExceedanceCurve linearPredictors makeReferenceMarginalDistribution MCS mex mexAll mexDependence mexMonteCarlo mexRangeFit migpd migpdCoefs mrl pegp3 pgev pglo pgpd pgumbel qegp3 qgev qglo qgpd qgumbel regp3 rFrechet rgev rglo rgpd rgumbel rl rMaxAR texmexFamily thinAndBurn weibull

Dependencies:cli cpp11 farver ggplot2 glue gtable isoband labeling lifecycle mvtnorm R6 RColorBrewer Rcpp rlang S7 scales vctrs viridisLite withr

Vignettes for texmex package

Rendered fromtexmexVignettes.Rmdusingknitr::rmarkdown

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

Help page	Topics
Extreme value modelling	texmex-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 ggplot	addExcesses
Information Criteria	AIC.evmOpt AIC.evmSim
Bootstrap a conditional multivariate extreme values model	bootmex plot.bootmex print.bootmex
Measures of extremal dependence	chi ggplot.chi plot.chi print.chi print.summary.chi summary.chi
Calculate the copula of a matrix of variables	copula copula.data.frame copula.default copula.matrix
Cross-validation for a model object	cv ggplot.cv plot.cv print.cv summary.cv
Cross-validation for the shape parameter in an extreme values model	cv.evmOpt
Density, cumulative density, quantiles and random number generation for the extended generalized Pareto distribution 3	degp3 pegp3 qegp3 regp3
Density, cumulative density, quantiles and random number generation for the generalized extreme value distribution	dgev pgev qgev rgev
Density, cumulative density, quantiles and random number generation for the generalized Pareto distribution	dgpd pgpd qgpd rgpd
The Gumbel distribution	dgumbel pgumbel qgumbel rgumbel
Compute empirical distribution function	edf
Estimate the EGP3 distribution power parameter over a range of thresholds	egp3RangeFit ggplot.egp3RangeFit plot.egp3RangeFit print.egp3RangeFit
Calculate upper end point for a fitted extreme value model	endPoint endPoint.evmBoot endPoint.evmOpt endPoint.evmSim
Extreme value modelling	evm evm.default evmReal
Bootstrap an evmOpt fit	coef.evmBoot evmBoot plot.evmBoot print.evmBoot print.summary.evmBoot summary.evmBoot
MCMC simulation around an evmOpt fit	evmSim
Set the seed from a fitted evmSim object.	evmSimSetSeed
Extremal index estimation and automatic declustering	bootExtremalIndex declust declust.default declust.extremalIndex evm.declustered extremalIndex extremalIndexRangeFit ggplot.extremalIndexRangeFit plot.declustered plot.extremalIndexRangeFit print.declustered print.extremalIndex
Fancy plotting for copulas	ggplot.copula
Diagnostic plots for an declustered object	ggplot.declustered ggplot.extremalIndex
Diagnostic plots for the replicate estimated parameter values in an evmBoot object	ggbootdensplots ggplot.evmBoot
Diagnostic plots for an evm object	ggplot.evmOpt ggplot.evmOpt, ggplot.hist.evmOpt ggplot.ppevm ggplot.qqevm ggplotrl
Diagnostic plots for the Markov chains in an evmSim object	ggacfplots ggdensplots ggplot.evmSim ggtraceplots
Conditional multivariate extreme values modelling	ggplot.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 models	ggplot.migpd migpd plot.migpd
Plotting function for return level estimation	ggplot.lp.evmBoot ggplot.lp.evmOpt ggplot.lp.evmSim ggplot.rl.evmBoot ggplot.rl.evmOpt ggplot.rl.evmSim
Profile likelihood based confidence intervals for GPD	gpd.prof
Estimate generalized Pareto distribution parameters over a range of values	ggplot.gpdRangeFit gpdRangeFit plot.gpdRangeFit print.gpdRangeFit print.summary.gpdRangeFit summary.gpdRangeFit
Joint exceedance curves	calcJointExceedanceCurve geom_jointExcCurve JointExceedanceCurve JointExceedanceCurve.default JointExceedanceCurve.mexMC JointExceedanceCurve.predict.mex print.jointExcCurve
Liver related laboratory data	liver
Log-likelihood for evmOpt objects	logLik.evmOpt
Provide full marginal reference distribution for for maringal transformation	makeReferenceMarginalDistribution
Multivariate conditional Spearman's rho	bootMCS 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 model	mexDependence
Simulation from dependence models	mexMonteCarlo
Estimate dependence parameters in a conditional multivariate extreme values model over a range of thresholds.	mexRangeFit
Change values of parameters in a migpd object	migpdCoefs
Mean residual life plot	ggplot.mrl mrl plot.mrl print.mrl print.summary.mrl summary.mrl
Plot copulas	plot.copula
Plots for evmOpt objects	plot.evmOpt
Plots for evmSim objects	plot.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 levels	plot.rl.evmBoot plot.rl.evmOpt plot.rl.evmSim print.rl.evmOpt rl rl.evmBoot rl.evmOpt rl.evmSim
Print evmOpt objects	print.evmOpt
Rain, wavesurge, portpirie and nidd datasets.	nidd portpirie rain rain, wavesurge and portpirie wavesurge
Extreme Value random process generation.	rFrechet
Generalized logistic distribution	dglo pglo qglo rglo
Extreme Value random process generation.	rMaxAR
Simulate from a fitted evm object	simulate.evmBoot simulate.evmOpt simulate.evmSim
Air pollution data, separately for summer and winter months	summer summer and winter data winter
Create families of distributions	cgpd 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

Package: texmex 2.4.9

texmex: Statistical Modelling of Extreme Values

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