R and C++ code for performing posterior inference for Bayesian Conditional Transformation models illustrated for three different applications.
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Updated
Nov 29, 2023 - R
R and C++ code for performing posterior inference for Bayesian Conditional Transformation models illustrated for three different applications.
Code for simulation studies in "An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures", Boe et al. (2021) (https://doi.org/10.1002/sim.9108)
tracking survival rate of new employees with a best fitted Cox Proportional Hazards model using 4 most significant personality traits
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