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The texmex package for R

Extreme value modelling with R. Includes univariate modelling using generalized Pareto, generalized extreme value, Weibull and Gumbel distributions, and the multivariate conditional approach of Heffernan and Tawn.

The package contains a test suite that depends on the testthat package. To use the test suite, install using 'R CMD INSTALL --install-tests' and then running 'devtools::test("texmex")' within R, where "texmex" points to the package location.

This work was partially funded by AstraZeneca.

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Install

install.packages('texmex')

Monthly Downloads

621

Version

2.4.9

License

GPL (>= 2)

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Repository

https://github.com/harrysouthworth/texmex

Maintainer

Harry Southworth

Last Published

March 4th, 2024

Functions in texmex (2.4.9)

Generalized logistic distribution

Compute empirical distribution function

.specfun.safe.product

Compute pmax(x y, -1) in such a way that zeros in x beat infinities in y.

Accurately compute log(1 + x) / x

Density, cumulative density, quantiles and random number generation for the extended generalized Pareto distribution 3

Density, cumulative density, quantiles and random number generation for the generalized Pareto distribution

The Gumbel distribution

Accurately compute log(1-exp(x))

Accurately compute (exp(x) - 1) / x

Density, cumulative density, quantiles and random number generation for the generalized extreme value distribution

Fancy plotting for copulas

Bootstrap an evmOpt fit

Diagnostic plots for the replicate estimated parameter values in an evmBoot object

Set the seed from a fitted evmSim object.

Calculate upper end point for a fitted extreme value model

MCMC simulation around an evmOpt fit

ggplot.declustered

Diagnostic plots for an declustered object

Extremal index estimation and automatic declustering

Estimate the EGP3 distribution power parameter over a range of thresholds

Extreme value modelling

ggplot.rl.evmOpt

Plotting function for return level estimation

Liver related laboratory data

Profile likelihood based confidence intervals for GPD

makeReferenceMarginalDistribution

Provide full marginal reference distribution for for maringal transformation

Log-likelihood for evmOpt objects

Diagnostic plots for an evm object

Estimate generalized Pareto distribution parameters over a range of values

Diagnostic plots for the Markov chains in an evmSim object

Conditional multivariate extreme values modelling

Estimate the dependence parameters in a conditional multivariate extreme values model

Mean residual life plot

Plots for evmOpt objects

Simulation from dependence models

Plots for evmSim objects

Print evmOpt objects

Change values of parameters in a migpd object

Estimate dependence parameters in a conditional multivariate extreme values model over a range of thresholds.

Predict return levels from extreme value models, or obtain the linear predictors.

Process Metropolis output from extreme value model fitting to discard unwanted observations.

Extreme Value random process generation.

Fit multiple independent generalized Pareto models

Extreme Value random process generation.

simulate.evmOpt

Simulate from a fitted evm object

rain, wavesurge and portpirie

Rain, wavesurge, portpirie and nidd datasets.

Create families of distributions

Extreme value modelling

Measures of extremal dependence

Cross-validation for the shape parameter in an extreme values model

Cross-validation for a model object

Annotate a threshold selection ggplot

Information Criteria

JointExceedanceCurve

Joint exceedance curves

Calculate the copula of a matrix of variables

Multivariate conditional Spearman's rho

summer and winter data

Air pollution data, separately for summer and winter months

Bootstrap a conditional multivariate extreme values model