A statistical learning method that tries to find the best set
  of predictors and interactions between predictors for modeling binary or
  quantitative response data in a decision tree. Several search algorithms
  and ensembling techniques are implemented allowing for finetuning the
  method to the specific problem. Interactions with quantitative
  covariables can be properly taken into account by fitting local
  regression models. Moreover, a variable importance measure for assessing
  marginal and interaction effects is provided. Implements the
  procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Logic Forest is an ensemble machine learning method that identifies important and interpretable combinations of binary predictors using logic regression trees to model complex relationships with an 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>.