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 24 logistic models including 13 individual models and 11 ensembles of models of logistic data. The package also returns 25 plots, 5 tables, and a summary report. The package automatically
    builds all 24 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 24 models.

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.

Functions for fitting a functional principal components logit regression model
	in four different situations: ordinary and filtered functional principal components
	of functional predictors, included in the model according to their variability
	explanation power, and according to their prediction ability by stepwise methods. The
	proposed methods were developed in Escabias et al (2004) 
	<doi:10.1080/10485250310001624738> and Escabias et al (2005)
	<doi:10.1016/j.csda.2005.03.011>.