Description: Empirical likelihood-based multiple testing for the analysis of experimental designs.
Details of the testing procedures are given in Kim et al. (2021) <arxiv:2112.09206>.

Details of the testing procedures are given in Kim et al. (2021) <arxiv:2112.09206>. Additional functions for regression analysis are included.

Description: Performs multiple empirical likelihood tests for regression analysis.
Much of its functionality and syntax mimics the corresponding base R functions.
The core computational routines are implemented using the 'Eigen' C++ library
and 'RcppEigen' interface, with OpenMP for parallel computation. Additional
functions are available for multiple testing for the analysis of experimental
designs. Details of the testing procedures are given in Kim et al. (2021)

Imports: methods, Rcpp (>= 1.0.8), stats
Suggests: spelling, covr, knitr, rmarkdown, testthat (>= 3.0.0)

person("Eunseop", "Kim", , "kim.7302@osu.edu", role = c("aut", "cre")),
person("Steven", "MacEachern", role = c("aut", "ths")),
person("Mario", "Peruggia", role = c("aut", "ths"))
Description: Performs multiple empirical likelihood tests for linear and
generalized linear models.  The core computational routines are
implemented using the 'Eigen' C++ library and 'RcppEigen' interface,
with OpenMP for parallel computation. Details of multiple testing
procedures are given in Kim et al. (2021) <arxiv:2112.09206>.
Suggests: covr, knitr, rmarkdown, spelling, testthat (>= 3.0.0)

person("Steven", "MacEachern", role = c("ctb", "ths")),
person("Mario", "Peruggia", role = c("ctb", "ths"))
generalized linear models. The core computational routines are
procedures are given in Kim, MacEachern, and Peruggia (2021)
Suggests: covr, ggplot2, knitr, microbenchmark, rmarkdown, spelling,

<arxiv:2112.09206>. This work was supported by the U.S. National
Science Foundation under Grants No. SES-1921523 and DMS-2015552.
Suggests: covr, ggplot2, knitr, microbenchmark, R.rsp, rmarkdown,

person("Mario", "Peruggia", role = c("ctb", "ths")),
person("Pierre", "Chausse", role = "rev"),
person("Alex", "Stringer", role = "rev")
generalized linear models. The package offers an easy-to-use interface
and flexibility in specifying hypotheses and calibration methods,
extending the framework to simultaneous inferences. The core
computational routines are implemented using the 'Eigen' C++ library
and 'RcppEigen' interface, with OpenMP for parallel computation.
Details of the testing procedures are given in Kim, MacEachern, and
Peruggia (2021) <arxiv:2112.09206>. This work was supported by the
U.S. National Science Foundation under Grants No.  SES-1921523 and