Automatically suggests a correction when a typo occurs.

Implements the Fourier cumulative sum (CUSUM) cointegration test 
    for detecting cointegration relationships in time series data with 
    structural breaks. The test uses Fourier approximations to capture smooth 
    structural changes and CUSUM statistics to test for cointegration stability.
    Based on methodology described in Zaghdoudi (2025) 
    <doi:10.46557/001c.144076>. The corrected Akaike Information Criterion 
    (AICc) is used for optimal frequency selection.

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/>.

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.