Two classification ensemble methods based on logic regression models.  LogForest() uses a bagging approach to construct an ensemble of logic regression models.  LBoost() uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

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.