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kdecopula

Kernel Smoothing for Bivariate Copula Densities

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>.

Functions (18)

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_pi

Nearest-neighbor bandwidth selection for the tapered transformation estimator

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

plot.kdecopula

Plotting kdecopula objects

predict.kdecopula

Prediction method for kdecop() fits

simulate.kdecopula

Simulate data from a kdecop() fit.

wdbc

Wisconsin Diagnostic Breast Cancer (WDBC)

kdecopula

Kernel Smoothing for Bivariate Copula Densities

v0.9.2

GPL-3

Authors

Thomas Nagler [aut, cre], Kuangyu Wen [ctb]

Initial release

kdecopula

Functions (18)

kdecopula

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