Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.

Implements variable selection for high dimensional datasets with a binary response
    variable using the EM algorithm.  Both probit and logit models are supported.  Also included 
    is a useful function to generate high dimensional data with correlated variables.

Under- and over-dispersed binary data are modeled using an extended Poisson 
 process model (EPPM) appropriate for binary data. A feature of the model is that the 
 under-dispersion relative to the binomial distribution only needs to be greater than
 zero, but the over-dispersion is  restricted compared to other distributional models  
 such as the beta and correlated binomials. Because of this, the examples focus on 
 under-dispersed data and how, in combination with the beta or correlated distributions,
 flexible models can be fitted to data displaying both under- and over-dispersion. Using
 Generalized Linear Model (GLM)  terminology, the functions utilize linear predictors for
 the probability of success and scale-factor with various link functions for p, and log 
 link for scale-factor, to fit a variety of models relevant to areas such as bioassay. 
 Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214> and 
 Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.

Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.

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.