Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
    API, but instead uses the API that the web dashboard uses to generate the graphs
    displayed on the dashboard after login at <http://www.fitbit.com>.

Connection to the 'Fitbit' Web API <https://dev.fitbit.com/build/reference/web-api/> by including 'ggplot2' Visualizations, 'Leaflet' and 3-dimensional 'Rayshader' Maps. The 3-dimensional 'Rayshader' Map requires the installation of the 'CopernicusDEM' R package which includes the 30- and 90-meter elevation data.

Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an  arbitrary angle and the fit will still succeed. Helper functions are  provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by  least squares" <https://people.cas.uab.edu/~mosya/cl/>; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI,  Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma  (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302.

Generates predictive distributions based on calibrating priors
    for various commonly used statistical models, including models with 
    predictors. Routines for densities, probabilities, quantiles, random
    deviates and the parameter posterior are provided. The predictions are
    generated from the Bayesian prediction integral, with priors chosen to
    give good reliability (also known as calibration). For homogeneous models,
    the prior is set to the right Haar prior, giving predictions which are
    exactly reliable. As a result, in repeated testing, the frequencies of
    out-of-sample outcomes and the probabilities from the predictions agree.
    For other models, the prior is chosen to give good reliability. Where
    possible, the Bayesian prediction integral is solved exactly. Where exact
    solutions are not possible, the Bayesian prediction integral is solved
    using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion.
    Optionally, the prediction integral can also be solved using posterior
    samples generated using Paul Northrop's ratio of uniforms sampling package
    ('rust'). Results are also generated based on maximum likelihood, for
    comparison purposes. Various model selection diagnostics and testing
    routines are included. Based on "Reducing reliability bias in assessments
    of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T.
    and Jewson, L. (2024); <doi:10.5194/ascmo-11-1-2025>.