R: Kernel Smoothing for Bivariate Copula Densities

# Kernel Smoothing for Bivariate Copula Densities

## Documentation for package ‘kdecopula’ version 0.9.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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)