evd-internal               package:evd               R Documentation

_I_n_t_e_r_n_a_l _F_u_n_c_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     The evd package contains many internal functions that are not
     designed to be called by the user.

     The generic functions 'dens', 'pp', 'qq' and 'rl' create the
     diagnostic plots generated by 'plot.uvevd'. Similarly, 'bvdens',
     'bvcpp' and 'bvdp' create the diagnostic plots generated by
     'plot.bvevd'.

     There are internal fitting, simulation, distribution and density
     functions for each bivariate and multivariate parametric model,
     which are called from functions such as 'rbvevd' and 'rmvevd'.
     There also exists internal functions for the calculation and
     plotting of dependence functions of  bivariate and trivariate
     models, which are called from 'abvdep' and 'atvdep'. The
     dependence functions are ultimately plotted by the low-level
     functions 'bvdepfn' and 'tvdepfn'.

     The function 'pcint' calculates profile confidence intervals, and
     is called from the function 'plot.profile.evd'. The fitting
     function 'fgev' calls the internal functions 'fgev.quantile' and
     'fgev.norm' for fits under different parameterizations. The
     fitting function 'fpot' calls the internal functions 'fpot.norm'
     and 'fpot.quantile'. Marginal transformations are executed using
     'mtransform'.

     The function 'ccop' calculates condition copulas (i.e. conditional
     distributions under uniform margins) for each bivariate parametric
     model. This is needed to create the conditional P-P plots
     generated by 'bvcpp'.

     The functions 'nsloc.transform', 'na.vals', 'bvpost.optim',
     'bvstart.vals' and 'sep.bvdata' are used in the fitting of
     bivariate models. The function 'mvalog.check' checks and
     transforms the 'asy' argument for the multivariate asymmetric
     model.

     For fitting bivariate threshold models, internal functions exist
     for the censored and (currently unimplemented) point process
     likelihoods, and each of these calls a further internal function
     corresponding to the specified model. The internal function
     'bvtpost.optim' is then used for post optimization processing.

