Description: 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.

Author: Simon L. Rinderknecht and Peter Reichert
Description: Fits Density Ratio Classes to elicited

Author: Simon L. Rinderknecht and Peter Reichert <peter.reichert@eawag.ch>

Title: Mean and Covariance Matrix Estimation under Heavy Tails
Description: Robust estimation methods for the mean vector and covariance matrix
from data (possibly containing NAs) under multivariate heavy-tailed
distributions such as angular Gaussian, Cauchy, and Student's t. Additionally,
a factor model structure can be specified for the covariance matrix.
Y. Sun, P. Babu, and D. P. Palomar (2014). Regularized Tyler’s Scatter
Estimator: Existence, Uniqueness, and Algorithms. IEEE Trans. on Signal
Y. Sun, P. Babu, and D. P. Palomar (2015). Regularized Robust Estimation of
Mean and Covariance Matrix Under Heavy-Tailed Distributions. IEEE Trans. on
Chuanhai Liu and Donald B. Rubin (1995).
ML estimation of the t-distribution using EM and its extensions, ECM and ECME.
R. Zhou, J. Liu, S. Kumar, and D. P. Palomar (2015).
Robust factor analysis parameter estimation.
Lecture Notes in Computer Science (LNCS). <arXiv:1909.12530>.
person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com"),
person("Rui", "Zhou", role =  "aut", email = "rzhouae@connect.ust.hk")
Maintainer: Daniel P. Palomar <daniel.p.palomar@gmail.com>
Suggests: knitr, ggplot2, prettydoc, reshape2, rmarkdown, R.rsp,