Software
ROCnReg allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve, several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. ROCnReg also provides functions to obtain ROC-based optimal threshold values using several criteria (e.g., the Youden index). For the Bayesian methods, ROCnReg provides tools for assessing model fit via posterior predictive checks, while the model choice can be carried out via several information criteria. Numerical and graphical outputs are provided for all methods. ROCnReg is the only package implementing Bayesian procedures for ROC curves.
Associated article was published in The R Journal.
If you use ROCnReg, please cite it as
Rodríguez-Álvarez & Inacio, “ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates”, The R Journal, 2021.
A 50 minutes talk about ROCnReg is available on Youtube.
My GitHub page also contains code for several articles.
DDPstar implements a flexible, versatile, and computationally tractable model for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. The model assumes an additive structure for the mean of each mixture component and the effects of continuous covariates are captured through smooth nonlinear functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. The proposed method can also easily deal with parametric effects of categorical covariates, linear effects of continuous covariates, interactions between categorical and/or continuous covariates, varying coefficient terms, and random effects.