Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.

Functions to access data from the 'Strava v3 API' <https://developers.strava.com/>.

Unified object oriented interface for multiple independent streams of random numbers from different sources.

As of 'RStudio' v1.3, the preferences in the Global Options
    dialog (and a number of other preferences that aren’t) are now saved
    in simple, plain-text JSON files. This package provides an interface
    for working with these 'RStudio' JSON preference files to easily make
    modifications without using the point-and-click option menus. This is
    particularly helpful when working on teams to ensure a unified
    experience across machines and utilizing settings for best practices.

Access the RStudio API (if available) and provide informative error
    messages when it's not.

Create, convert and install custom 'RStudio' editor themes
    from 'Visual Studio Code', 'Positron' and 'TextMate' theme files.
    Convert themes between 'TextMate', 'Visual Studio Code' and 'Positron'
    formats and install bundled ports of popular themes for use in
    'RStudio'.

Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several 
    distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of 
    distributions, also known as the generalized error distribution.
    Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where 
    the likelihood is maximized by several optimization procedures using the 'GNU Scientific Library (GSL)', translated to 'C++' 
    code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric
    Laplace distributions.