R is great for installing software.  Through the 'installr'
    package you can automate the updating of R (on Windows, using updateR())
    and install new software. Software installation is initiated through a
    GUI (just run installr()), or through functions such as: install.Rtools(),
    install.pandoc(), install.git(), and many more. The updateR() command
    performs the following: finding the latest R version, downloading it,
    running the installer, deleting the installation file, copy and updating
    old packages to the new R installation.

Similar to 'rstantools' for 'rstan',
  the 'instantiate' package builds pre-compiled
  'CmdStan' models into CRAN-ready statistical modeling R packages.
  The models compile once during installation,
  the executables live inside the file systems of their respective packages,
  and users have the full power and convenience of
  'cmdstanr' without any additional compilation after package installation.
  This approach saves time and helps R package developers
  migrate from 'rstan' to the more modern 'cmdstanr'.
  Packages 'rstantools', 'cmdstanr', 'stannis', and
  'stanapi' are similar Stan clients with different objectives.

Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.

Calculates insulin secretion rates from C-peptide values
    based on the methods described in Van Cauter et al. (1992)
    <doi:10.2337/diab.41.3.368>. Includes functions to calculate estimated
    insulin secretion rates using linear or cubic spline interpolation of
    c-peptide values (see Eaton et al., 1980 <doi:10.1210/jcem-51-3-520>
    and Polonsky et al., 1986 <doi:10.1172/JCI112308>) and to calculate
    estimates of input coefficients (volume of distribution, short half life,
    long half life, and fraction attributed to short half life) as described by
    Van Cauter. Although the generated coefficients are specific to insulin
    secretion, the two-compartment secretion model used here is useful for
    certain applications beyond insulin.