Barnard's unconditional test for 2x2 contingency tables.

Provides tools to visualize ordination results in 'R' by adding covariance-based ellipses, centroids, vectors, and confidence regions to plots created with 'ggplot2'. 
    The package extends the 'vegan' framework and supports Principal Component Analysis (PCA), Redundancy Analysis (RDA), and Non-metric Multidimensional Scaling (NMDS). 
    Ellipses can represent either group dispersion (standard deviation, SD) or centroid precision (standard error, SE), following Wang et al. (2015) <doi:10.1371/journal.pone.0118537>. 
    Robust estimators of covariance are implemented, including the Minimum Covariance Determinant (MCD) method of Hubert et al. (2018) <doi:10.1002/wics.1421>. 
    This approach reduces the influence of outliers. 
    barrel is particularly useful for multivariate ecological datasets, promoting reproducible, publication-quality ordination graphics with minimal effort.

Calculate the bark beetle phenology based on raster data or
    point-related data. There are multiple models implemented for two bark
    beetle species. The models can be customized and their submodels (onset of
    infestation, beetle development, diapause initiation, mortality) can be
    combined. The following models are available in the package:
    PHENIPS-Clim (first-time release in this package),
    PHENIPS (Baier et al. 2007) <doi:10.1016/j.foreco.2007.05.020>,
    RITY (Ogris et al. 2019) <doi:10.1016/j.ecolmodel.2019.108775>,
    CHAPY (Ogris et al. 2020) <doi:10.1016/j.ecolmodel.2020.109137>,
    BSO (Jakoby et al. 2019) <doi:10.1111/gcb.14766>,
    Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>,
    Jönsson et al. (2011) <doi:10.1007/s10584-011-0038-4>.
    The package may be expanded by models for other bark beetle species in the
    future.

Provides the 'C++' header-only library 'barry' for use in R packages. 
 'barry' is a 'C++' template library for counting sufficient statistics on binary 
 arrays and building discrete exponential-family models. It provides tools for 
 sparse arrays, user-defined count statistics, support set constraints, 
 power set generation, and includes modules for Discrete Exponential Family Models 
 (DEFMs) and network statistics. By placing these headers in this package, we 
 offer an efficient distribution system for CRAN as replication of this code in 
 the sources of other packages is avoided. This package follows the same 
 approach as the 'BH' package which provides 'Boost' headers for R packages.