Bayesian Exposure-Response Analysis Tools
Transform to draws objects
BayesERtools: Bayesian Exposure-Response Analysis Tools
Build specifications for covariate effect simulation/visualization
Calculate median and quantile intervals from ersim object
Internal functions for developing an ER model with covariates for bina...
Perform covariate selection for linear ER model
Exposure metrics selection for linear ER models
Develop linear ER model for binary or continuous endpoint
Exposure metrics selection for Emax models
Develop Emax model for continuous and binary endpoint
Customize specifications for covariate effect simulations/visualizatio...
S3 methods for the classes ermod_bin_cov_sel
S3 methods for the classes ermod_exp_sel
S3 methods for the classes ermod_*
S3 methods for the classes ersim_* and ersim_med_qi_*
Evaluate exposure-response model prediction performance
Extract credible interval of the exposure coefficient
Extract elements from an object of class ermod_*
Extract elements from objects of the classes ersim_* and `ersim_med_...
Extract elements from S3 objects
Run k-fold cross-validation
Efficient approximate leave-one-out cross-validation (LOO)
Probability of Direction (pd)
Plot variable selection performance
Visualize the covariate effects for ER model
Plot exposure metric selection comparison
Default GOF plot for ER model
Plot ER model simulations
Format the covariate effect simulation results for printing
Summarize the priors used for linear or linear logistic regression mod...
Perform simulation of covariate effects for ER model
Calculate marginal expected response for specified exposure values
Simulate from ER model at specified exposure values
Simulate from ER model
Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at <https://genentech.github.io/BayesERbook/>.