Dealing with Binary Replicates
Compute the average-/median- or MAP-based prevalence estimates based o...
Bayesian computations
Fit the Bayesian model for Binary Replicates
BinaryReplicates: Dealing with Binary Replicates
Perform classification on the scores for each fold of a cvEM object
Classification based on a thresholding of the scores
Cross-validation for the EM algorithm
Compute the Maximum-A-Posteriori estimate with the EM algorithm
A mammography dataset
Non-Bayesian scoring methods
Compute predictive Bayesian scores
Statistical methods for analyzing binary replicates, which are noisy binary measurements of latent binary states. Provides scoring functions (average, median, likelihood-based, and Bayesian) to estimate the probability that an individual is in the positive state. Includes maximum a posteriori estimation via the EM algorithm and full Bayesian inference via Stan. Supports classification with inconclusive decisions and prevalence estimation.
Useful links