Functions to easily convert data to binary formats other programs/machines can understand.

It provides utility functions for investigating changes within R
    packages. The pkgInfo() function extracts package information such as
    exported and non-exported functions as well as their arguments. The
    pkgDiff() function compares this information for two versions of a package
    and creates a diff file viewable in a browser.

Search for R packages on CRAN directly from the R console, based on the packages' titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', 'or'), view detailed information on any package and keep track of the latest package contributions to CRAN. If you don't want to search from the R console, use the comfortable R Studio add-in.

Provides essential checklists for R package developers, whether
    you're creating your first package or beginning a new project. This tool
    guides you through each step of the development process, including specific
    considerations for submitting your package to the Comprehensive R Archive
    Network (CRAN). Simplify your workflow and ensure adherence to best
    practices with 'packagepal'.

Helper functions for package creation, building and
    maintenance. Designed to work with a build system such as 'GNU make' or
    package 'fakemake' to help you to conditionally work through the stages of
    package development (such as spell checking, linting, testing, before
    building and checking a package).

Compute and visualize the cross-sectional and longitudinal number
    and rank percentile of package downloads from Posit/RStudio's CRAN mirror.

Algorithms to find arrangements of non-overlapping circles.

A collection of functions to construct Markov model for model-based 
    cost-effectiveness analysis. This includes creating Markov model (both time
    homogenous and time dependent models), decision analysis, sensitivity 
    analysis (deterministic and probabilistic). The package allows estimation 
    of parameters for the Markov model from a given individual patient level 
    data, provided the data file follows some standard data entry rules.