Function-oriented Make-like declarative pipelines for
  Statistics and data science are supported in the 'targets' R package.
  As an extension to 'targets', the 'tarchetypes' package provides
  convenient user-side functions to make 'targets' easier to use.
  By establishing reusable archetypes for common kinds of
  targets and pipelines, these functions help express complicated
  reproducible pipelines concisely and compactly.
  The methods in this package were influenced by the 'targets' R package.
  by Will Landau (2018) <doi:10.21105/joss.00550>.

Runs 'targets' pipelines bundled inside a package and caches
    the results in the R user cache directory, so that users of the package
    do not need to rerun the pipeline themselves. Package authors can update
    the cached results at any time by releasing a new package version.

Measure text's sentiment with dictionaries and simple rules covering
    negations and modifiers. User-supplied dictionaries are supported, including
    Unicode emojis and multi-word tokens, so this package can also be used to
    study constructs beyond sentiment.

Various methods for targeted and semiparametric inference including
	     augmented inverse probability weighted (AIPW) estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
         variable importance and conditional average treatment effects (CATE)
         (van der Laan (2006) <doi:10.2202/1557-4679.1008>),
	     estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
         linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Pipeline tools coordinate the pieces of computationally
  demanding analysis projects.
  The 'targets' package is a 'Make'-like pipeline tool for statistics and
  data science in R. The package skips costly runtime for tasks
  that are already up to date,
  orchestrates the necessary computation with implicit parallel computing,
  and abstracts files as R objects. If all the current output matches
  the current upstream code and data, then the whole pipeline is up
  to date, and the results are more trustworthy than otherwise.
  The methodology in this package
  borrows from GNU 'Make' (2015, ISBN:978-9881443519)
  and 'drake' (2018, <doi:10.21105/joss.00550>).