Routines for fitting Logic Regression models. Logic Regression is described
	in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte
        Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005)
        <DOI:10.1002/gepi.20042>.

Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.

Framework for adding authentication to 'shiny' applications.
    Provides flexibility as compared to other options for where user
    credentials are saved, allows users to create their own accounts, and
    password reset functionality. Bryer (2024)
    <doi:10.5281/zenodo.10987876>.

Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys 
	prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
	solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary
	maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available
	in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.

Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.

An implementation of a method of extending a logistic regression
    model beyond linear effects of the co-variates. The extension in is
    constructed by first equating the logistic regression model to a naive Bayes
    model where all the margins are specified to follow natural exponential 
    distributions conditional on Y, that is, a model for Y given X that is
    specified through the distribution of X given Y, where the columns of X are
    assumed to be mutually independent conditional on Y. Subsequently, the
    model is expanded by adding vine - copulas to relax the assumption of
    mutual independence, where pair-copulas are added in a stage-wise, forward
    selection manner. Some heuristics are employed during the process of
    selecting edges, as well as the families of pair-copula models. After each
    component is added, the parameters are updated by a (smaller) number of
    gradient steps to maximise the likelihood. When the algorithm has stopped
    adding edges, based the criterion that a new edge should improve the
    likelihood more than k times the number new parameters, the parameters are
    updated with a larger number of gradient steps, or until convergence.