Constructs confidence intervals on the probability of
        success in a binomial experiment via several parameterizations.

Twelve confidence intervals for one binomial proportion or a vector of binomial proportions are computed. The confidence intervals are: Jeffreys,  Wald, Wald corrected, Wald, Blyth and Still,  Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. <doi:10.1002/sim.4780120902>.

The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.

Detection of a statistically significant trend in the data provided by the user.
    This is based on the a signed test based on the binomial distribution.
    The package returns a trend test value, T, and also a p-value. A T value 
    close to 1 indicates a rising trend, whereas a T value close to -1 indicates a decreasing
    trend. A T value close to 0 indicates no trend. There is also a command to visualize
    the trend. A test data set called gtsa_data is also available, which has global mean 
    temperatures for January, April, July, and October for the years 1851 to 2022.
    Reference: Walpole, Myers, Myers, Ye. (2007, ISBN: 0-13-187711-9).

Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) <doi:10.1167/tvst.9.3.8>. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included 'shiny' apps.