Empirical likelihood ratio tests and confidence intervals for means/quantiles/hazards
 	from possibly censored and/or truncated data. In particular, the empirical likelihood
        for the Kaplan-Meier/Nelson-Aalen estimator. Now does AFT regression too.

Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis 
            (or multiple mean-type hypotheses) based on two samples with possible censored data.

Test hypotheses and construct confidence intervals for AUC (area under Receiver 
             Operating Characteristic curve) and pAUC (partial area under ROC curve), from the
			 given two samples of test data with disease/healthy subjects. The method used is
			 based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in
			 <https://www.ms.uky.edu/~mai/research/eAUC1.pdf>.

Collection of functions related to benchmark with prediction models
	 for data analysis and editing of clinical and epidemiological data.

Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.

Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.

It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.