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.

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>.

Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.