Functions that facilitate and speed up the analysis of data
    produced by a Syntech servosphere <http://www.ockenfels-syntech.com/products/locomotion-compensation/>,
    which is equipment for studying the movement behavior of arthropods.
    This package is designed to make working with data produced from a 
    servosphere easy for someone new to or unfamiliar with R. The functions
    provided in this package fall into three broad-use categories: functions for
    cleaning raw data produced by the servosphere software, functions for
    deriving movement variables based on position data, and functions for 
    summarizing movement variables for easier analysis. These functions are
    built with functions from the tidyverse package to work efficiently, as a
    single servosphere file may consist of hundreds of thousands of rows of data
    and a user may wish to analyze hundreds of files at a time. Many of the 
    movement variables derivable through this package are described in the 
    following papers:
    Otálora-Luna, Fernando; Dickens, Joseph C. (2011) <doi:10.1371/journal.pone.0020990>
    Party, Virginie; Hanot, Christophe; Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013) <doi:10.1371/journal.pone.0052897>
    Bell, William J.; Kramer, Ernest (1980) <doi:10.1007/BF01402908>
    Becher, Paul G; Guerin, Patrick M. (2009) <doi:10.1016/j.jinsphys.2009.01.006>.

Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.

Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix. 
             This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools. 
             The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone. 
             The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.