Hirk, RainerHornik, KurtVana, Laura2025-06-172025-06-172020-01-011548-766010.18637/jss.v093.i04https://trapdev.rcub.bg.ac.rs/handle/123456789/769137The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with a multivariate probit and a multivariate logit link. A flexible modeling framework for multiple ordinal measurements on the same subject is set up, which takes into consideration the dependence among the multiple observations by employing different error structures. Heterogeneity in the error structure across the subjects can be accounted for by the package, which allows for covariate dependent error structures. In addition, different regression coefficients and threshold parameters for each response are supported. If a reduction of the parameter space is desired, constraints on the threshold as well as on the regression coefficients can be specified by the user. The proposed multivariate framework is illustrated by means of a credit risk application.OPEN102022 Softwareentwicklung101018 Statistik101018 Statisticscomposite likelihood estimationStatisticsrcorrelated ordinal datacomposite likelihood estimation, correlated ordinal data, multivariate ordinal logit regression model, multivariate ordinal probit regression model, R102022 Software developmentcomposite likelihood estimation; correlated ordinal data; multivariate ordinal logit regression model; multivariate ordinal probit regression model; Rmultivariate ordinal logit regression modelHA1-4737multivariate ordinal probit regression modelpublication05 social sciences0502 economics and businessdoi_dedup___:7e3d54fd39ef2f3e0eb051b334986e0d