Generalized logistic distribution
Compute empirical distribution function
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
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
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
Plot copulas
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.
Return levels
Fit multiple independent generalized Pareto models
Extreme Value random process generation.
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
Joint exceedance curves
Calculate the copula of a matrix of variables
Multivariate conditional Spearman's rho
Air pollution data, separately for summer and winter months
Bootstrap a conditional multivariate extreme values model