R: Estimation for Binary Emax Models with Missing Responses and Bias Reduction

# Estimation for Binary Emax Models with Missing Responses and Bias Reduction

## Documentation for package ‘ememax’ version 0.1.0

- DESCRIPTION file
- Package README

## Help Pages

Augment_Missing | Augment missing response with observed information
Comp_Hess | Compute analytical form of Hessian matrix of Binary Emax model
Comp_I | Compute analytical form of expected information matrix of Binary Emax model
comp_Q | Maximization function estimation of EM algorithm with defined weight.
comp_Q_firth | Maximization function estimation of bias reduced EM algorithm with defined weight.
comp_theta | Estimation of emax parameters in EM algorithm iteration.
comp_theta_cox_snell | Cox–Snell bias-corrected estimator (one-step using 'clinDR' MLE)
comp_theta_firth | Estimation of emax parameters in Jeffery's prior penalized IL algorithm iteration.
comp_theta_firth_score | Firth-corrected estimating equation solution (score-based)
comp_theta_jeffrey | Jeffreys-penalized likelihood estimator via Newton–Raphson
comp_vcov | Calculate the variance covariance matrix of estimated parameters by EmaxEM
comp_vcov_firth | Calculate the variance covariance matrix of estimated parameters by EmaxEM_firth
comp_weight | Estimation of working weight in EM algorithm iteration.
fitEmaxEM | Fitting IL method with Emax model and binary response missing data.
fitEmaxEM_firth | Fitting bias reduced IL method with Emax model and binary response missing data.
log_Emax_i | Log likelihood estimation of binary Emax model
log_missing_i | Log likelihood estimation of logisitic missing indicator model
sim_data | Simulate dataset for testing Emaxem and Emaxem_firth