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.

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).

Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically
    builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and
    information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as
    True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver
    Operating Characteristic (ROC) curves for each of the 18 models.