Performs alignment, PCA, and modeling of multidimensional and 
    unidimensional functions using the square-root velocity framework 
    (Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014 
    <DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic 
    analysis of functional data through phase and amplitude separation.

Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.

Regression models for functional data, i.e.,
    scalar-on-function, function-on-scalar and function-on-function
    regression models, are fitted by a component-wise gradient boosting
    algorithm.  For a manual on how to use 'FDboost', see Brockhaus,
    Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.

Provides the probability density function (PDF), cumulative
  distribution function (CDF), the first-order and second-order partial
  derivatives of the PDF, and a fitting function for the diffusion decision
  model (DDM; e.g.,
  Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial
  variability in the drift rate. Because the PDF, its partial derivatives, and
  the CDF of the DDM both contain an infinite sum, they need to be approximated.
  'fddm' implements all published approximations
  (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>;
  Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>;
  Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>;
  Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus
  new approximations. All approximations are implemented purely in 'C++'
  providing faster speed than existing packages.