Title: Estimates Mixture Models for Case-Control Data
Author: Michelle Winerip, Garrick Wallstrom, Joshua LaBaer
Description: Estimates non-Gaussian mixture models of case-control data. The four types of models supported are binormal, two component constrained, two component unconstrained, and four component.  The most general model is the four component model, under which both cases and controls are distributed according to a mixture of two unimodal distributions.  In the four component model, the two component distributions of the control mixture may be distinct from the two components of the case mixture distribution.  In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls.  In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components.  In the binormal model, cases and controls are distributed according to distinct unimodal distributions.  These models assume that Box-Cox transformed case and control data with a common lambda parameter are distributed according to Gaussian mixture distributions.   Model parameters are estimated using the expectation-maximization (EM) algorithm.  Likelihood ratio test comparison of nested models can be performed using the lr.test function. AUC and PAUC values can be computed for the model-based and empirical ROC curves using the auc and pauc functions, respectively. The model-based and empirical ROC curves can be graphed using the roc.plot function. Finally, the model-based density estimates can be visualized by plotting a model object created with the bimixt.model function.

Package: binGroup
Title: Evaluation and experimental design for binomial group testing
Version: 0.3-1
Author: Frank Schaarschmidt
Description: Confidence intervals and tests for a single binomial proportion.
Clopper-Pearson, Blaker, Second-order corrected,  Wilson, Agresti-Coull and Wald CI for standard binomial and binomial group testing.  Exact, Score and Wald Test for standard binomial  and binomial group testing.
Experimental design 1. depending on power of the different CI methods and 2. controlling bias and mse of the estimator
depending on group size and sample size, and calculation of expected interval width.
Maintainer: Frank Schaarschmidt <schaarschmidt@biostat.uni-hannover.de>
License: GPL
Packaged: Sat Feb  3 16:03:04 2007; schaa164