Multiple Empirical Likelihood Tests
ConfregEL class
CEL class
Chi-square statistic
Model coefficients
Confidence interval for model parameters
Confidence region for model parameters
ControlEL class
Convergence check
Critical value
EL class
Control parameters for computation
Empirical likelihood for general estimating functions
Empirical likelihood for generalized linear models
Empirical likelihood for linear models
Empirical likelihood for the mean
Empirical likelihood for the standard deviation
ELD class
Empirical likelihood displacement
ELMT class
Empirical likelihood multiple tests
ELT class
Empirical likelihood test
Degrees of freedom
Optimization results
GLM class
LM class
Empirical log-likelihood
Empirical log-likelihood ratio
Log probabilities
melt: Multiple Empirical Likelihood Tests
Number of observations in a model
Plot methods
Print methods
-value
QGLM class
SD class
Significance tests
Summary methods
SummaryEL class
SummaryELMT class
SummaryELT class
SummaryGLM class
SummaryLM class
SummaryQGLM class
Model weights
Performs multiple empirical likelihood tests. It 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 provided in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.
Useful links