Defines the functions used to compute the
  bimodal index as defined by Wang et al. (2009)
  <https://pmc.ncbi.nlm.nih.gov/articles/PMC2730180/>,
  <doi:10.4137/CIN.S2846>.

Provides likelihood-based and hierarchical estimation methods
    for thresholded (binomial-probit) data. Supports fixed-mean and random-mean
    models with maximum likelihood estimation (MLE), generalized linear mixed model
    (GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations.
    For methodological background, see Albert and Chib (1993)
    <doi:10.1080/01621459.1993.10476321> and McCulloch (1994)
    <doi:10.2307/2297959>.

R client to the 'Binance' Public Rest API for data collection on cryptocurrencies, portfolio management and trading: <https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.

Provides methods for the binarization of one-dimensional data and some visualization functions.

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.

Tools to create binary dosage from either VCF
    or GEN files, merge binary dosage files, and read
    binary dosage files.

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>.