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.

Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.

Functions to build, evaluate, and visualize insurance rating 
    models. It simplifies the process of modeling premiums, and allows to 
    analyze insurance risk factors effectively. The package employs a 
    data-driven strategy for constructing insurance tariff classes, drawing on 
    the work of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.