Implements methods for calibrating an aggregated functional data model using 
    wavelets or splines. Each aggregated curve is modeled as a linear combination of 
    component functions and known weights. The component functions are estimated using 
    wavelets or splines. The package is based on dos Santos Sousa (2024) <doi:10.1515/mcma-2023-2016>, 
    Saraiva and Dias (2009) <doi:10.47749/T/UNICAMP.2009.471073> and Johnstone and Silverman (1997) <doi:10.1111/1467-9868.00071>.

Provides functional tools such as fmap(), fwalk(), and fapply() 
    to iterate over vectors, data frames, or grouped data with optional parallelism 
    and real-time progress tracking. Designed for readable and reproducible workflows, 
    including support for Monte Carlo simulations and benchmarking.

Find functions in an unstructured directory and explore their dependencies. 
    Sourcing of R source files is performed without side-effects: from R scripts that have 
    executable code and function definitions only functions are sourced.

S4 classes for univariate and multivariate functional data with
    utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description 
    of the package functionalities and its interplay with the MFPCA package for 
    multivariate functional principal component analysis 
    <https://CRAN.R-project.org/package=MFPCA>.

Computes six functional diversity indices. These are namely,
  Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness
  (FRic), Functional Richness intersections (FRic_intersect), Functional
  Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008
  <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions
  to compute functional diversity indices
  (preprint: <doi:10.32942/osf.io/dg7hw>).

The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>.
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights.
    Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices:
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568.
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF)
    covariance matrix of standardized regression coefficients: theoretical extensions
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>.
    Waller, N. G.  (2018).  Direct Schmid-Leiman transformations and rank-deficient loadings matrices.  Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.