Implementation of the tests for rotational symmetry on the
    hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020)
    <doi:10.1080/01621459.2019.1665527>. The package also implements the
    proposed distributions on the hypersphere, based on the tangent-normal
    decomposition, and allows for the replication of the data application
    considered in the paper.

Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva,
    L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans.
    Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.

Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.  References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009),
    Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009),
    Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010),
    Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006),
    Hartley, R and Aftab, K and Trumpf, J. (2011),
    Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013),
    Maonton, Jonathan (2004), 
    Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), 
    Rancourt, D. and Rivest, L.P. and Asselin, J. (2000),
    Chang, Ted and Rivest, Louis-Paul (2001), 
    Fisher, Nicholas I. (1996, ISBN:0521568900).

A rotatogram is a method of displaying an association which is axis non-dominant. This is achieved in two ways: First, the method of estimating the slope and intercept uses the least-products method rather than more typical least squared error for the "dependent" variable. The least products method has no "dependent" variable and is scale independent. Second, the plot is rotated such that the resulting regression line is vertical, reducing the suggestion that the vertical axis is the dominant one. The slope can be read relative to either axis equally.