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CopulaRegression (version 0.1-5)

Bivariate Copula Based Regression Models

Description

This R-packages presents a bivariate, copula-based model for the joint distribution of a pair of continuous and discrete random variables. The two marginal random variables are modeled via generalized linear models, and their joint distribution (represented by a parametric copula family) is estimated using maximum-likelihood techniques.

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Version

Install

install.packages('CopulaRegression')

Monthly Downloads

70

Version

0.1-5

License

GPL (>= 2.0)

Maintainer

Nicole Kraemer

Last Published

September 4th, 2014

Functions in CopulaRegression (0.1-5)

ztp.glm

GLM for a zero truncated Poisson variable
loglik_joint

Loglikelihood of the joint regression model
pgam

Distribution of a Gamma variable
pztp

Cumulative distribution function of a zero truncated Poisson variable
epolicy_loss

Expectation of the policy loss
density_joint

Joint density of X and Y
predict.copreg

Prediction of the copula regression model
rgam

Samples from a Gamma variable
simulate_regression_data

Simulate regression data
dgam

Density of a Gamma variable
dpolicy_loss

Density of the policy loss
theta2z

Transformation of the copula parameter
z2theta

Inverse of the parameter transformation
vuongtest

Model comparison using a Vuong test
density_conditional

Conditional density of Y given X
CopulaRegression-package

Bivariate copula-based regression models
D_u

H-function of the copula
qpolicy_loss

Quantile of the policy loss
mle_joint

ML-Estimates of the joint model.
ppolicy_loss

Cumaltive distribution function of the policy loss
mle_marginal

ML-estimates of the marginal models
simulate_joint

Simulation from the joint model
copreg

Joint, copula-based regression model
dztp

Density of a zero truncated Poisson variable