R: Empirical Likelihood Analysis for the Cox Model and Yang-Prentice (2005) Model

# Empirical Likelihood Analysis for the Cox Model and Yang-Prentice (2005) Model

## Documentation for package ‘ELYP’ version 0.7-5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

BJfindL2 | Find the Wilks Confidence Interval Lower Bound for Betafun from the 2 dimensional Buckley-James Empirical Likelihood Ratio Function
BJfindU2 | Find the Wilks Confidence Interval Upper Bound for Betafun from the 2 dimensional Buckley-James Empirical Likelihood Ratio Function
CoxEL | Compute Empirical Likelihood and Partial Likelihood of Cox model for Testing the beta and Baseline Jointly.
CoxFindL2 | Find the Wilks Confidence Interval Lower Bound for Efun based on the Empirical Likelihood Ratio Function CoxEL
CoxFindL3 | Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio Function
CoxFindU2 | Find the Wilks Confidence Interval Upper Bound for Efun from the Empirical Likelihood Ratio Function CoxEL( ).
CoxFindU3 | Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio Function
ELrange | Find the Ractangular parameter region where EL is Only 4 below the Maximum Value.
findL2d | Find the Wilks Confidence Interval Lower Bound from the Given 2-d Empirical Likelihood Ratio Function
findL3 | Find the Wilks Confidence Interval Lower Bound from the Given Empirical Likelihood Ratio Function
findL4 | Find the Wilks Confidence Interval Lower Bound from the Given Empirical Likelihood Ratio Function
findU2d | Find the Wilks Confidence Interval Upper Bound from the Given 2-d Empirical Likelihood Ratio Function
findU3 | Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio Function
findU32 | Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio Function
findU4 | Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio Function
fitYP3 | Compute Baseline Hazard for the Given Data, Given Parameters: beta1, beta2, lam, and fun. Also, Given the Baseline, Compute the empirical likelihood value.
fitYP4 | Compute Alpha and Baseline Hazard for the Given Data, Given Parameters beta1, beta2. Also, compute the empirical likelihood value.
fitYP41 | Compute the Baseline Hazard for the Given Data, given Parameters beta1, beta2. Also, compute the empirical likelihood value.
GastricCancer | Gastric Cancer Data
myLLfun | Compute Baseline Hazard for the Given Data and Parameters beta1, beta2, lam. Also Compute the empirical likelihood value.
myLLfun2 | Compute Baseline Hazard for the Given Data and Parameters beta1, beta2, alpha, lam. Also Compute the empirical likelihood value.
Pfun | The Hazard Ratio in YP Model as a Function of beta1 beta2 and Mulam.
Pfun2 | The Hazard Ratio in YP Model as a Function of beta1, beta2, a, X, and Mulam.
simuDataYP | Generate random times that follow the YP model with the Given Parameters th1, th2, and alphaX.
smallcell | Smallcell Lung Cancer Data