Conditional Probit Model for Analysing Continuous Outcomes

Documentation for package ‘cprobit’ version 1.0.2

bg_variability	Inpatient blood glucose data for 1200 patients
compile_est	Inpernal function: generate commonly used summary statistics for estimates.
cprobit	Apply the three-step workflow for the analysis of two repeated outcomes from each subject
cprobit_step1	Inpernal function: step 1 of the proposed workflow
estimate_sd_error	Inpernal function: estimate the SD of error terms in the difference model
geom_mean	Inpernal function: compute geometric mean of a positive variable
get_v	Inpernal function: compute difference in the (transformed) outcome
make_design_mat	Inpernal function: construct design matrix without the intercept term.
print.cprobit	Apply the three-step workflow for the analysis of two repeated outcomes from each subject
profile_llh	Inpernal function: profile log-likelihood of lambda
summary.cprobit	Apply the three-step workflow for the analysis of two repeated outcomes from each subject
update_estimate	Inpernal function: update Step 1 estimates to obtain linear exposure effect on (transformed) outcome