  The functional logit regression model was proposed by Escabias et al. (2004)
with the objective of modeling a scalar binary response variable from a
functional predictor. The model estimation proposed in that case was performed
in a subspace of L2(T) of squared integrable functions of finite dimension,
generated by a finite set of basis functions. For that estimation it was
assumed that the curves of the functional predictor and the functional
parameter of the model belong to the same finite subspace. The estimation so
obtained was affected by high multicollinearity problems and the solution given
to these problems was based on different functional principal component
analysis. The logitFD package introduced here provides a toolbox for the fit of
these models by implementing the different proposed solutions and by
generalizing the model proposed in 2004 to the case of several functional and
non-functional predictors. The performance of the functions is illustrated by
using data sets of functional data included in the fda.usc package from R-CRAN.
