Package: ELYP 0.7-5

Mai Zhou

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

Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters.

Authors:Mai Zhou

ELYP_0.7-5.tar.gz
ELYP_0.7-5.tar.gz(r-4.5-noble)ELYP_0.7-5.tar.gz(r-4.4-noble)
ELYP_0.7-5.tgz(r-4.4-emscripten)ELYP_0.7-5.tgz(r-4.3-emscripten)
ELYP.pdf |ELYP.html✨
ELYP/json (API)

# Install 'ELYP' in R:
install.packages('ELYP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 23 scripts 166 downloads 2 mentions 36 exports 3 dependencies

Last updated 6 years agofrom:fa37050a54. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 11 2024
R-4.5-linux-x86_64OKDec 11 2024

Exports:BJfindL2BJfindU2CoxELCoxFindL2CoxFindL3CoxFindU2CoxFindU3cumsumsurvELcompELrangefindL2dfindL3findL4findU2dfindU3findU32findU4fitYP3fitYP4fitYP41Haz3Haz4myffitYP3myffitYP4myffitYP41myffitYP411myffitYP412myLLfunmyLLfun2PfunPfun2rYPsimuDataYPYP3YP4YP41

Dependencies:latticeMatrixsurvival

Readme and manuals

Help Manual

Help pageTopics
Find the Wilks Confidence Interval Lower Bound for Betafun from the 2 dimensional Buckley-James Empirical Likelihood Ratio FunctionBJfindL2
Find the Wilks Confidence Interval Upper Bound for Betafun from the 2 dimensional Buckley-James Empirical Likelihood Ratio FunctionBJfindU2
Compute Empirical Likelihood and Partial Likelihood of Cox model for Testing the beta and Baseline Jointly.CoxEL
Find the Wilks Confidence Interval Lower Bound for Efun based on the Empirical Likelihood Ratio Function CoxELCoxFindL2
Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio FunctionCoxFindL3
Find the Wilks Confidence Interval Upper Bound for Efun from the Empirical Likelihood Ratio Function CoxEL( ).CoxFindU2
Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio FunctionCoxFindU3
Find the Ractangular parameter region where EL is Only 4 below the Maximum Value.ELrange
Find the Wilks Confidence Interval Lower Bound from the Given 2-d Empirical Likelihood Ratio FunctionfindL2d
Find the Wilks Confidence Interval Lower Bound from the Given Empirical Likelihood Ratio FunctionfindL3
Find the Wilks Confidence Interval Lower Bound from the Given Empirical Likelihood Ratio FunctionfindL4
Find the Wilks Confidence Interval Upper Bound from the Given 2-d Empirical Likelihood Ratio FunctionfindU2d
Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio FunctionfindU3
Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio FunctionfindU32
Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio FunctionfindU4
Compute Baseline Hazard for the Given Data, Given Parameters: beta1, beta2, lam, and fun. Also, Given the Baseline, Compute the empirical likelihood value.fitYP3
Compute Alpha and Baseline Hazard for the Given Data, Given Parameters beta1, beta2. Also, compute the empirical likelihood value.fitYP4
Compute the Baseline Hazard for the Given Data, given Parameters beta1, beta2. Also, compute the empirical likelihood value.fitYP41
Gastric Cancer DataGastricCancer
Compute Baseline Hazard for the Given Data and Parameters beta1, beta2, lam. Also Compute the empirical likelihood value.myLLfun
Compute Baseline Hazard for the Given Data and Parameters beta1, beta2, alpha, lam. Also Compute the empirical likelihood value.myLLfun2
The Hazard Ratio in YP Model as a Function of beta1 beta2 and Mulam.Pfun
The Hazard Ratio in YP Model as a Function of beta1, beta2, a, X, and Mulam.Pfun2
Generate random times that follow the YP model with the Given Parameters th1, th2, and alphaX.simuDataYP
Smallcell Lung Cancer Datasmallcell