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>.

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.

Setaria viridis (green foxtail) is a common weed.  This
    package contains measurements from individual branches of a wild
    Setaria viridis plant collected near the author's home.  The data is
    intended for use in data analysis practice.

The implementation of a forecasting-specific tree-based model that is in particular suitable for global time series forecasting, as proposed in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the concept of Self Exciting Threshold Autoregressive (SETAR) models to define the node splits and thus, the model is named SETAR-Tree. The SETAR-Tree uses some time-series-specific splitting and stopping procedures. It trains global pooled regression models in the leaves allowing the models to learn cross-series information. The depth of the tree is controlled by conducting a statistical linearity test as well as measuring the error reduction percentage at each node split. Thus, the SETAR-Tree requires minimal external hyperparameter tuning and provides competitive results under its default configuration. A forest is developed by extending the SETAR-Tree. The SETAR-Forest combines the forecasts provided by a collection of diverse SETAR-Trees during the forecasting process.