A figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).

Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.

Preprocess numeric data matrices into desired transformed representations.
    Standardization, Unitization, Cubitization and adaptive intervals are offered.

In this record linkage package, data preprocessing has been meticulously executed
  to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. 
  This approach facilitates seamless data integration and analysis across various datasets. While users 
  have the flexibility to modify variable names, the system intelligently ensures that 
  changes are only permitted when they do not compromise data consistency or essential variable essence.

An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.

Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.

Provides a collection of templates to author preregistration documents for scientific studies in PDF format.

Preregistrations, or more generally, registrations, enable
    explicit timestamped and (often but not necessarily publicly) frozen
    documentation of plans and expectations as well as decisions and
    justifications. In research, preregistrations are commonly used to
    clearly document plans and facilitate justifications of deviations from
    those plans, as well as decreasing the effects of publication bias by
    enabling identification of research that was conducted but not published.
    Like reporting guidelines, (pre)registration forms often have specific
    structures that facilitate systematic reporting of important items. The
    'preregr' package facilitates specifying (pre)registrations in R and
    exporting them to a human-readable format (using R Markdown partials or
    exporting to an 'HTML' file) as well as human-readable embedded data
    (using 'JSON'), as well as importing such exported (pre)registration
    specifications from such embedded 'JSON'.