Implements fractional differencing with Autoregressive Moving Average models to analyse long-memory 	time series data. Traditional ARIMA models typically use integer values for differencing, which are 	suitable for time series with short memory or anti-persistent behaviour. In contrast, the Fractional ARIMA 	model allows fractional differencing, enabling it to effectively capture long memory characteristics in 	time series data. The ‘fracARMA’ package is user-friendly and allows users to manually input the 	fractional differencing parameter, which can be obtained using various estimators such as the GPH 	estimator, Sperio method, or Wavelet method and many. Additionally, the package enables users to directly 	feed the time series data, AR order, MA order, fractional differencing parameter, and the proportion of 	training data as a split ratio, all in a single command. The package is based on the reference from the 	paper of Irshad and others (2024, <doi:10.22271/maths.2024.v9.i6b.1906>).

Maximum likelihood estimation of the parameters of a fractionally
   differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989);
   including inference and basic methods.  Some alternative algorithms to estimate "H".

Calculate numerical asymptotic distribution functions of likelihood ratio 
    statistics for fractional unit root tests and tests of cointegration rank. 
    For these distributions, the included functions calculate critical values 
    and P-values used in unit root tests, cointegration tests, and rank tests 
    in the Fractionally Cointegrated Vector Autoregression (FCVAR) model.
    The functions implement procedures for tests described in the following articles:
    Johansen, S. and M. Ø. Nielsen (2012) <doi:10.3982/ECTA9299>,
    MacKinnon, J. G. and M. Ø. Nielsen (2014) <doi:10.1002/jae.2295>.

Aim is to provide fractional Brownian vector field generation algorithm, Hurst parameter estimation method and fractional kriging model for multivariate data modeling.