Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
    A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
    First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied.
    As of version 1.1, this package includes B-splines, Fourier bases and polynomials.

Automatically suggests a correction when a typo occurs.

Estimation and inference using the Fractionally Cointegrated 
    Vector Autoregressive (VAR) model. It includes functions for model specification, 
    including lag selection and cointegration rank selection, as well as a comprehensive
    set of options for hypothesis testing, including tests of hypotheses on the 
    cointegrating relations, the adjustment coefficients and the fractional 
    differencing parameters. 
    An article describing the FCVAR model with examples is available on the Webpage 
    <https://sites.google.com/view/mortennielsen/software>.

Computes different multidimensional FD indices.  Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances.  Also contains other useful tools for functional ecology.

These functions were developed to support functional data
 analysis as described in Ramsay, J. O. and Silverman, B. W.
 (2005) Functional Data Analysis. New York: Springer and in 
 Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). 
 Functional Data Analysis with R and Matlab (Springer). 
 The package includes data sets and script files working many examples 
 including all but one of the 76 figures in this latter book.  Matlab versions 
 are available by ftp from 
 <https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.