Provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. It also provides utilities that recommends a normalisation technique based on the distribution of the data.

Functions to make board game graphics with the 'ggplot2', 'grid', 'rayrender', 'rayvertex', and 'rgl' packages.  Specializes in game diagrams, animations, and "Print & Play" layouts for the 'piecepack' <https://www.ludism.org/ppwiki> but can make graphics for other board game systems.  Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, 'piecepack', playing cards, etc.

Implements piecewise structural equation modeling from a single
    list of structural equations, with new methods for non-linear, latent, and
    composite variables, standardized coefficients, query-based prediction and
    indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more.

Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. The pie glyphs are independent of the axes and plot dimensions, to prevent distortions when the plot dimensions are changed.

Imputes missing species trait data for comparative analyses
    by combining three sources of information: phylogenetic similarity
    (closely related species share similar traits), cross-trait
    correlations (observed traits inform missing ones), and optional
    environmental covariates (climate, habitat, geography). Handles
    continuous measurements, counts, binary variables, ordered categories,
    unordered categories, bounded proportions, zero-inflated counts, and
    compositional multi-proportion data in a single call. The method blends a
    phylogenetic baseline with a graph neural network correction; a
    per-trait gate calibrated on held-out data ensures the network only
    contributes when it improves on the baseline. Provides conformal
    prediction intervals for continuous, count, and ordinal traits and an
    experimental analysis-aware multiple-imputation workflow for one missing
    continuous covariate in Gaussian linear, binomial-logit, and Gaussian
    random-intercept models, with Rubin pooling limited to fixed effects.
    Stochastic graph-network and posterior-tree completions are prediction
    diagnostics rather than validated inferential imputations. Tested up to
    10,000 species.
    Bundled datasets include 300-species and 9,993-species bird-trait subsets
    with matching example phylogenetic trees.
    Rubin (1987, ISBN:978-0-471-08705-2); Vovk et al. (2005,
    ISBN:978-0-387-25061-8); Nakagawa and de Villemereuil (2019)
    <doi:10.1093/sysbio/syy089>.