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.

Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.

Implements various methods for estimating fractal dimension of time series and 2-dimensional data <doi:10.1214/11-STS370>.

The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.

Various functions for performing fractal and multifractal analysis including performing fractal regression. Please refer to Peng and colleagues (1994) <doi:10.1103/physreve.49.1685>, Kantelhardt and colleagues (2002)<doi:10.1016/S0378-4371(02)01383-3>, and Likens and colleagues (2019) <doi:10.1016/j.physa.2019.121580>.