Evaluation and Experimental Design for Binomial Group Testing
Accuracy measures for informative Dorfman testing
Operating characteristics for array testing without master pooling
Expected value of order statistics from a beta distribution
Confidence Intervals for One Proportion in Binomial Group Testing
Power to Reject a Hypothesis in Binomial Group Testing for One Proport...
Hypothesis Test for One Proportion in Binomial Group Testing
Confidence Interval for One Proportion in Group Testing with Variable ...
Expected Width of Confidence Intervals in Binomial Group Testing
Confidence Intervals for One Binomial Proportion
Sample Size Iteration for One Parameter Binomial Problem
Statistical Methods for Group Testing.
Power Calculation for One Parameter Binomial Problem
Hypothesis tests for One Binomial Proportion
Expected Confidence Interval Width for One Binomial Proportion
Testing expenditure for informative Dorfman testing
Sample Size Iteration Depending on Minimal MSE in One-Parameter Group ...
Auxiliary for Controlling Group Testing Regression
Fitting Group Testing Models Under the Halving Protocol
Fitting Group Testing Models in Matrix Pooling Setting
Fitting Group Testing Models
Operating characteristics for hierarchical group testing
Find the optimal testing configuration for informative array testing w...
Find the optimal testing configuration for informative three-stage hie...
Operating characteristics for informative two-stage hierarchical (Dorf...
Find the optimal testing configuration for informative two-stage hiera...
Arrange a matrix of probabilities for informative array testing
Operating characteristics for array testing with master pooling
Iterate Sample Size in One Parameter Group Testing
Find the optimal testing configuration for non-informative array testi...
Find the optimal testing configuration for non-informative array testi...
Find the optimal testing configuration for non-informative three-stage...
Find the optimal testing configuration for non-informative two-stage h...
Find the characteristics of an informative two-stage hierarchical (Dor...
Find the optimal pool size for Optimal Dorfman or Thresholded Optimal ...
Find the optimal testing configuration
Generate a vector of probabilities for informative group testing algor...
Plot Results of nDesign or sDesign
Plot Results of binDesign
Diagnostic line fit for pool.bin objects
Find the optimal pool sizes for Pool-Specific Optimal Dorfman (PSOD) t...
Confidence intervals for a single proportion
Confidence intervals for the difference of proportions
Predict Method for Group Testing Model Fits
Print Functions for Group Testing CIs and Tests for One Proportion
Print Functions for nDesign and sDesign
Print Function for binDesign
Print methods for objects of classes "gt" and "gt.mp"
Print methods for classes "poolbin" and "poolbindiff"
Print Functions for summary.gt.mp and summary.gt
Extract Model Residuals From a Fitted Group Testing Model
Iterate Group Size for a One-Parameter Group Testing Problem
Simulation Function for Group Testing Data
Simulation Function for Group Testing Data for the Halving Protocol
Simulation Function for Group Testing Data with Matrix Pooling Design
Summary Method for Group Testing Model (Matrix Pooling) Fits
Summary Method for Group Testing Model (Simple Pooling) Fits
Summary methods for "poolbin" and "poolbindiff"
Find the optimal threshold value for Thresholded Optimal Dorfman testi...
Methods for estimation and hypothesis testing of proportions in group testing designs: methods for estimating a proportion in a single population (assuming sensitivity and specificity equal to 1 in designs with equal group sizes), as well as hypothesis tests and functions for experimental design for this situation. For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations.