The 'ESTIMATE' package infers tumor purity from expression data as a 
  function of immune and stromal infiltrate, but requires writing of intermediate 
  files, is un-pipeable, and performs poorly when presented with modern datasets 
  with current gene symbols. 'tidyestimate' a fast, tidy, modern reimagination of
  'ESTIMATE' (2013) <doi:10.1038/ncomms3612>.

Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.

Helper functions for empirical research in financial
    economics, addressing a variety of topics covered in Scheuch, Voigt,
    and Weiss (2023) <doi:10.1201/b23237>.  The package is designed to
    provide shortcuts for issues extensively discussed in the book,
    facilitating easier application of its concepts. For more information
    and resources related to the book, visit
    <https://www.tidy-finance.org/r/index.html>.

An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.

A toolkit of tidy data manipulation verbs with 'data.table' as the backend.
  Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 
  'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain.
  This package is an extension of 'data.table'. While enjoying a tidy syntax, 
  it also wraps combinations of efficient functions to facilitate frequently-used data operations.

Tidy syntax for 'data.table', using modification by reference whenever possible.
 This toolkit is designed for big data analysis in high-performance desktop or laptop computers.
 The syntax of the package is similar or identical to 'tidyverse'.
 It is user friendly, memory efficient and time saving. For more information,
 check its ancestor package 'tidyfst'.

Provides functions that compute predictions from Generalised
    Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble.
    These can be plotted with a generic plot()-method that uses 'ggplot2' or
    plotted as any other data frame. The main function is predict_gam().