Title: A Seamless Integration of 'Gretl' and 'R'
Description: 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.
Imports: knitr (>= 1.20), rmarkdown, kableExtra

Imports: knitr (>= 1.20), rmarkdown, kableExtra, magrittr

Title: Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data
Author: Nan Xiao <nanx@uchicago.edu>, Gao Wang <gaow@uchicago.edu>
Description: Convert the 'Ensembl' gene identifiers from Genotype-Tissue Expression (GTEx) data to identifiers in other annotation systems, including 'Entrez', 'HGNC', and 'UniProt'.

Author: Nan Xiao <me@nanx.me>, Gao Wang <gaow@uchicago.edu>, Lei Sun <sunl@uchicago.edu>

person("Gao", "Wang", email = "gaow@uchicago.edu", role = "aut"),
person("Lei", "Sun", email = "sunl@uchicago.edu", role = "aut")
Description: Convert 'Ensembl' gene identifiers from Genotype-Tissue Expression (GTEx) data to identifiers in other annotation systems, including 'Entrez', 'HGNC', and 'UniProt'.

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

Title: Toolbox for Model Selection and Combinations for the Forecasting
Authors@R: person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
comment="Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK")
Description: Implements model selection and combinations via information criteria based on
the values of partial correlations. This allows, for example, solving "fat regression"
problems, where the number of variables is much larger than the number of observations.
This is driven by the research on information criteria, which is well discussed in
Burnham & Anderson (2002) <doi:10.1007/b97636>, and currently developed further by
Ivan Svetunkov and Yves Sagaert (working paper in progress). 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.
Imports: forecast, stats, graphics, utils, zoo
Suggests: smooth, numDeriv, testthat, rmarkdown, knitr
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics
and Forecasting, Lancaster University, UK)