Kernel Smoothing for Bivariate Copula Densities

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Documentation for package ‘kdecopula’ version 0.9.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

kdecopula-package	Kernel Smoothing for Bivariate Copula Densities
bw_bern	Bandwidth selection for the Bernstein copula estimator
bw_beta	Bandwidth selection for the beta kernel estimator
bw_mr	Bandwidth selection for the mirror-reflection estimator
bw_t	Bandwidth selection for the transformation kernel estimator
bw_tll	Bandwidth selection for the transformation local likelihood estimator
bw_tll_nn	Nearest-neighbor bandwidth selection for the transformation local likelihood estimator
bw_tt_cv	Nearest-neighbor bandwidth selection for the tapered transformation estimator
bw_tt_pi	Nearest-neighbor bandwidth selection for the tapered transformation estimator
contour.kdecopula	Plotting 'kdecopula' objects
dep_measures	Dependence measures of a 'kdecop()' fit
dkdecop	Working with 'kdecopula' objects
fitted.kdecopula	Extract fitted values from a 'kdecop()' fits.
hkdecop	H-function and inverse of a 'kdecop()' fit
kdecop	Bivariate kernel copula density estimation
kdecopula	Kernel Smoothing for Bivariate Copula Densities
logLik.kdecopula	Log-Likelihood of a 'kdecopula' object
pkdecop	Working with 'kdecopula' objects
plot.kdecopula	Plotting 'kdecopula' objects
predict.kdecopula	Prediction method for 'kdecop()' fits
rkdecop	Working with 'kdecopula' objects
simulate.kdecopula	Simulate data from a 'kdecop()' fit.
wdbc	Wisconsin Diagnostic Breast Cancer (WDBC)