A "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.

Methods to "add" two R tables; also an alternative
     interpretation of named vectors as generalized R tables, so that
     c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3).  Uses
     'disordR' discipline (Hankin, 2022,
     <doi:10.48550/arXiv.2210.03856>).  Extraction and replacement
     methods are provided.  The underlying mathematical structure is
     the Free Abelian group, hence the name.  To cite in publications
     please use Hankin (2023) <doi:10.48550/arXiv.2307.13184>.

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".