Kernel Smoothing for Bivariate Copula Densities
Bandwidth selection for the beta kernel estimator
Bandwidth selection for the mirror-reflection estimator
Bandwidth selection for the Bernstein copula estimator
Log-Likelihood of a kdecopula object
Dependence measures of a kdecop() fit
Working with kdecopula objects
Extract fitted values from a kdecop() fits.
H-function and inverse of a kdecop() fit
Bivariate kernel copula density estimation
Kernel Smoothing for Bivariate Copula Densities
Plotting kdecopula objects
Prediction method for kdecop() fits
Simulate data from a kdecop() fit.
Bandwidth selection for the transformation kernel estimator
Bandwidth selection for the transformation local likelihood estimator
Nearest-neighbor bandwidth selection for the transformation local like...
Nearest-neighbor bandwidth selection for the tapered transformation es...
Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling, see Nagler (2018) <doi:10.18637/jss.v084.i07>.