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.

Quantify the serial correlation across lags of a given functional 
    time series using the autocorrelation function and a partial autocorrelation
    function for functional time series proposed in 
    Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>.
    The autocorrelation functions are based on the L2 norm of the lagged covariance 
    operators of the series. Functions are available for estimating the 
    distribution of the autocorrelation functions under the assumption 
    of strong functional white noise.

Implementations of the k-means, hierarchical agglomerative and 
    DBSCAN clustering methods for functional data which allows for jointly 
    aligning and clustering curves. It supports functional data defined on 
    one-dimensional domains but possibly evaluating in multivariate codomains. 
    It supports functional data defined in arrays but also via the 'fd' and 
    'funData' classes for functional data defined in the 'fda' and 'funData' 
    packages respectively. It currently supports shift, dilation and affine 
    warping functions for functional data defined on the real line and uses the 
    SRSF framework to handle boundary-preserving warping for functional data 
    defined on a specific interval. Main reference for the k-means algorithm: 
    Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment 
    for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for 
    the SRSF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) 
    "Generative models for functional data using phase and amplitude separation" 
    <doi:10.1016/j.csda.2012.12.001>.