Package index • endogCopula

Getting started

Package overview and the generic entry point that dispatches to each estimator by name.

endogCopula endogCopula-package: endogCopula: Gaussian Copula Based Endogeneity Corrections
copreg(): Generic entry point for the copula endogeneity estimators

The six Gaussian copula based endogeneity corrections for cross-sectional linear models.

CopRegPG(): Copula endogeneity correction of Park and Gupta (2012) (PG)
CopReg2sCOPE(): Copula endogeneity correction of Yang, Qian and Xie (2025) (2sCOPE)
CopReg2sCOPEnp(): Copula endogeneity correction of Hu, Qian and Xie (2025) (2sCOPE-np)
CopRegIMA(): Copula endogeneity correction of Haschka (2025) (IMA)
CopRegBMW(): Copula endogeneity correction of Breitung, Mayer and Wied (2024) (BMW)
CopRegJAMS(): Copula endogeneity correction of Liengaard et al. (2025) (JAMS)

Diagnostics for the identification requirements behind each estimator.

validity(): Check identification assumptions of a fitted endogeneity correction
validity(<copreg>): Validity check for a fitted copreg model
print(<copreg.validity>): Print a copreg validity check

Extractors and S3 methods for objects returned by the estimators.

coef(<copreg>) vcov(<copreg>) nobs(<copreg>) formula(<copreg>): Extract components of a fitted copreg model
summary(<copreg>): Summarise a fitted copreg model
print(<summary.copreg>): Print a copreg model summary
print(<copreg>): Print a fitted copreg model
confint(<copreg>): Confidence intervals for a copreg model
residuals(<copreg>): Residuals of a fitted copreg model
fitted(<copreg>): Fitted values of a copreg model
predict(<copreg>): Predict from a fitted copreg model
update(<copreg>): Update and refit a copreg model

Simulated data used in the examples and vignettes.

Internal helpers shared with the endogCopulaPanel and endogCopulaBayes companion packages. Documented for reference, not for direct use.

.copreg_cdf_choices .copreg_ties_choices .cdf_estimate() .copreg_model() .copreg_export_names() .ad_test() .ks_normal() .copreg_diagnostics() .wald() .skewness() .ex_kurtosis() .validity_nonnormality(): Internal helpers shared with the companion packages