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.

Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.

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>.

Randomization of presence/absence species distribution raster
    data with or without including spatial structure for calculating
    standardized effect sizes and testing null hypothesis. The
    randomization algorithms are based on classical algorithms for
    matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster
    data.

Query and print information about the current R
    session.  It is similar to 'utils::sessionInfo()', but includes more
    information about packages, and where they were installed from.

More easy to get intersection, union or complementary set and 
    combinations.