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.

A system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).

Dimensionality reduction techniques for binary data including
    logistic PCA.

Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. <https://psyarxiv.com/gq7az/>. The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline.

Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.