Package: kdecopula 0.9.1

Thomas Nagler

kdecopula: Kernel Smoothing for Bivariate Copula Densities

Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling.

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

# Install 'kdecopula' in R:

install.packages('kdecopula', repos = c('https://tnagler.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

Datasets:

On CRAN:

Last updated 7 years agofrom:82ad25c8da. Checks:7 OK, 4 NOTE. Indexed: yes.

Last update: 2017-01-14
Started: 2016-01-20

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