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.

Extends the fitdistr() function (of the MASS package) with several functions 
  to help the fit of a parametric distribution to non-censored or censored data. 
  Censored data may contain left censored, right censored and interval censored values, 
  with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), 
  the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit 
  estimation (MGE) and maximum spacing estimation (MSE) methods (available only for 
  non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. 
  Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction 
  to parametric estimation.