Bayesian Misclassified Failure (MF) Survival Model
BayesMFSurv with Bomin Kim and Bumba Mukherjee.
This package contains functions to fit parametric (Weibull and Exponential) Misclassified-Failure Survival models via Bayesian methods, as described in Bagozzi et al (2018, working paper). The Bayesian Misclassified-Failure Model accounts for asymmetric misclassification arising within one’s failed cases. More specifically, the model is designed to accommodate situations where true censored cases are misclassified as failed observations. The package also contains auxiliary functions to calculate various statistics using model results.
The three functions in the package are:
||Fits a parametric Bayesian MF model via Markov Chain Monte Carlo to estimate the misclassification in the first stage and the hazard in the second stage.|
||Calculates the loglikelihood for fitted model objects of class bayes.mfsurv.|
||Calculates the deviance information criterion (DIC) for fitted model objects of class bayes.mfsurv.|
Bayesian Spatial Cure Model
BayesSPCure with Bomin Kim and Bumba Mukherjee.
This package presents and implements the Bayesian Spatial Cure Parametric Survival models, as described in Joo and Mukherjee (2018). Functions to t Bayesian Spatial Cure Parametric Survival models via Markov Chain Monte Carlo (MCMC) are contained in the package.
Zero-inflated, Middle-inflated, and Top-inflated Ordered Probit Models
ZMTiOP with Benjamin Bagozzi and Bumba Mukherjee.
R package for MLE of Zero-inflated, Middle-inflated, and Top-inflated Ordered Probit Models With and Without Correlated Errors.