Provides a syntax to create and combine Gaussian process kernels 
  in 'greta'. You can then them to define either full rank or sparse Gaussian 
  processes. This is an extension to the 'greta' software, Golding (2019) 
  <doi:10.21105/joss.01601>.

The social network literature features numerous methods for assigning
    value to paths as a function of their ties. 'gretel' systemizes these approaches,
    casting them as instances of a generalized path value function indexed by 
    a penalty parameter. The package also calculates probabilistic path value and
    identifies optimal paths in either value framework. Finally, proximity 
    matrices can be generated in these frameworks that capture high-order connections 
    overlooked in primitive adjacency sociomatrices. Novel methods are described
    in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. 
    More traditional methods are also implemented, as described in Yang, Knoke (2001) 
    <doi:10.1016/S0378-8733(01)00043-0>.

It allows running 'gretl' (<http://gretl.sourceforge.net/index.html>) program from R, R Markdown and Quarto. 'gretl' ('Gnu' Regression, 'Econometrics', and Time-series Library) is a statistical software for Econometric analysis.  This package does not only integrate 'gretl' and 'R' but also serves  as a 'gretl' Knit-Engine for 'knitr' package. Write all your 'gretl' commands in 'R', R Markdown chunk.

Convert 'Ensembl' gene identifiers from Genotype-Tissue
    Expression (GTEx) data to identifiers in other annotation systems,
    including 'Entrez', 'HGNC', and 'UniProt'.

Implements functions and instruments for regression model building and its
             application to forecasting. The main scope of the package is in variables selection
             and models specification for cases of time series data. This includes promotional
             modelling, selection between different dynamic regressions with non-standard
             distributions of errors, selection based on cross validation, solutions to the fat
             regression model problem and more. Models developed in the package are tailored
             specifically for forecasting purposes. So as a results there are several methods
             that allow producing forecasts from these models and visualising them.