kdengpdcon
(Package: evmix) :
Kernel Density Estimate and GPD Tail Extreme Value Mixture Model With
Density, cumulative distribution function, quantile function and random number generation for the extreme value mixture model with kernel density estimate for bulk distribution upto the threshold and conditional GPD above threshold with continuity at threshold. The parameters are the bandwidth lambda, threshold u GPD shape xi and tail fraction phiu.
fbetagpd
(Package: evmix) :
MLE Fitting of beta Bulk and GPD Tail Extreme Value Mixture Model
Maximum likelihood estimation for fitting the extreme value mixture model with beta for bulk distribution upto the threshold and conditional GPD above threshold. With options for profile likelihood estimation for threshold and fixed threshold approach.
fgammagpd
(Package: evmix) :
MLE Fitting of Gamma Bulk and GPD Tail Extreme Value Mixture Model
Maximum likelihood estimation for fitting the extreme value mixture model with gamma for bulk distribution upto the threshold and conditional GPD above threshold. With options for profile likelihood estimation for threshold and fixed threshold approach.
fpsden
(Package: evmix) :
MLE Fitting of P-splines Density Estimator
Maximum likelihood estimation for P-splines density estimation. Histogram binning produces frequency counts, which are modelled by constrained B-splines in a Poisson regression. A penalty based on differences in the sequences B-spline coefficients is used to smooth/interpolate the counts. Iterated weighted least squares (IWLS) for a mixed model representation of the P-splines regression, conditional on a particular penalty coefficient, is used for estimating the B-spline coefficients. Leave-one-out cross-validation deviances are available for estimation of the penalty coefficient.
kdengpd
(Package: evmix) :
Kernel Density Estimate and GPD Tail Extreme Value Mixture Model
Density, cumulative distribution function, quantile function and random number generation for the extreme value mixture model with kernel density estimate for bulk distribution upto the threshold and conditional GPD above threshold. The parameters are the bandwidth lambda, threshold u GPD scale sigmau and shape xi and tail fraction phiu.
mgammagpd
(Package: evmix) :
Mixture of Gammas Bulk and GPD Tail Extreme Value Mixture Model
Density, cumulative distribution function, quantile function and random number generation for the extreme value mixture model with mixture of gammas for bulk distribution upto the threshold and conditional GPD above threshold. The parameters are the multiple gamma shapes mgshape, scales mgscale and mgweights, threshold u GPD scale sigmau and shape xi and tail fraction phiu.
itmgng
(Package: evmix) :
Normal Bulk with GPD Upper and Lower Tails Interval Transition Mixture Model
Density, cumulative distribution function, quantile function and random number generation for the extreme value mixture model with normal for bulk distribution between the upper and lower thresholds with conditional GPD's for the two tails and interval transition. The parameters are the normal mean nmean and standard deviation nsd, interval half-width espilon, lower tail (threshold ul, GPD scale sigmaul and shape xil and tail fraction phiul) and upper tail (threshold ur, GPD scale sigmaur and shape xiR and tail fraction phiuR).
fitmnormgpd
(Package: evmix) :
MLE Fitting of Normal Bulk and GPD Tail Interval Transition Mixture Model
Maximum likelihood estimation for fitting the extreme value mixture model with the normal bulk and GPD tail interval transition mixture model. With options for profile likelihood estimation for threshold and interval half-width, which can both be fixed.
evmix.diag
(Package: evmix) :
Diagnostic Plots for Extreme Value Mixture Models
The classic four diagnostic plots for evaluating extreme value mixture models: 1) return level plot, 2) Q-Q plot, 3) P-P plot and 4) density plot. Each plot is available individually or as the usual 2x2 collection.
providing 
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