Efficient Bayesian generalized linear models with time-varying coefficients 
    as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial 
    observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using 
    Hamiltonian Monte Carlo provided by Stan, using a state space representation 
    of the model in order to marginalise over the coefficients for efficient sampling. 
    For non-Gaussian models, the package uses the importance sampling type estimators based on 
    approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).

Easily collect walk scores, bike scores, and transit scores (where
    available) from the 'Walk Score' API <https://www.walkscore.com/professional/api.php>, 
    a proprietary API that assigns locations a walkability score between 0 and 100.

A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.

The 'shiny' application Wallace is a modular platform for 
    reproducible modeling of species niches and distributions. Wallace
    guides users through a complete analysis, from the acquisition of species
    occurrence and environmental data to visualizing model predictions on an 
    interactive map, thus bundling complex workflows into a single, 
    streamlined interface. An extensive vignette, which guides users through 
    most package functionality can be found on the package's GitHub Pages 
    website: <https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html>.