Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.

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.