Learn R Programming

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.

Copy Link

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)

GLM for a zero truncated Poisson variable

Loglikelihood of the joint regression model

Distribution of a Gamma variable

Cumulative distribution function of a zero truncated Poisson variable

Expectation of the policy loss

Joint density of X and Y

Prediction of the copula regression model

Samples from a Gamma variable

simulate_regression_data

Simulate regression data

Density of a Gamma variable

Density of the policy loss

Transformation of the copula parameter

Inverse of the parameter transformation

Model comparison using a Vuong test

density_conditional

Conditional density of Y given X

CopulaRegression-package

Bivariate copula-based regression models

H-function of the copula

Quantile of the policy loss

ML-Estimates of the joint model.

Cumaltive distribution function of the policy loss

ML-estimates of the marginal models

Simulation from the joint model

Joint, copula-based regression model

Density of a zero truncated Poisson variable