Description: Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Author: Edmunds Cers <edmunds.cers@gmail.com>, Janis Valeinis <valeinis@lu.lv>

person("Janis", "Valeinis", role = "aut", email = "janis.valeinis@lu.lv", comment = c(ORCID = "0000-0003-0989-0444")),
person("Edmunds", "Cers", role = "aut", email = "edmunds.cers@gmail.com"),
person("Janis", "Gredzens", role = "cre", email = "janis.gredzens@lu.lv"))
Description: Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.
Author: Janis Valeinis [aut] (<https://orcid.org/0000-0003-0989-0444>),
Packaged: 2022-05-07 15:58:37 UTC; jgredzens

Title: Empirical Likelihood (EL) for Comparing Two Survival Functions
Description: Functions for computing critical values and implementing the one-sided/two-sided EL tests.
Author: Hsin-wen Chang [aut, cre] <hwchang@stat.sinica.edu.tw>

Title: The Empirical Likelihood-Based Consistent Information Criterion
Description: We developed a consistent and robust information criterion to conduct model selection for semiparametric models. It is free of distribution specification and powerful to locate the true model given large sample size. This package provides several usage of ELCIC with applications in generalized linear model (GLM), generalized estimating equation (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanism of missing at random and drop-out. Chixaing Chen, Ming Wang, Rongling Wu, Runze Li (2020) <doi:10.5705/ss.202020.0254>.
Suggests: knitr, rmarkdown, markdown, testthat (>= 3.0.0)
Packaged: 2022-02-13 19:47:23 UTC; chixiang.chen