Automates sum coding (also known as effect coding) for 
    Ordinary Least Squares (OLS) regression models. This approach is 
    specifically designed to handle seasonal time series and categorical 
    variables by comparing each group to the grand mean, rather than a 
    single baseline category. This ensures that the intercept represents 
    the unweighted grand mean of the dependent variable. For a comprehensive 
    overview of contrast coding systems, see the UCLA Advanced Research 
    Computing documentation (2021) <https://stats.oarc.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/>.

Reproducible, programmatic retrieval of survey datasets from the
    Pew Research Center.

Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.

Eco-phylogenetic and community phylogenetic analyses.
    Keeps community ecological and phylogenetic data matched up and
    comparable using 'comparative.comm' objects. Wrappers for common
    community phylogenetic indices ('pez.shape', 'pez.evenness',
    'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
    of Cavender-Bares (2004) correlation of phylogenetic and
    ecological matrices ('fingerprint.regression'). Phylogenetic
    Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
    Helmus (2011) and Rafferty & Ives (2013). Simulation of null
    assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').

Implements Penalized Fast Causal Inference (PFCI), a two-stage
    causal structure learning procedure for high-dimensional settings with
    potential latent variables and selection bias. In the first stage,
    neighborhood selection via the Lasso constructs a sparse undirected
    skeleton. In the second stage, the Fast Causal Inference (FCI) algorithm
    orients edges on this reduced graph, producing a Partial Ancestral Graph
    (PAG) that accounts for latent confounders. The method is consistent
    under sparsity assumptions and substantially faster than standard FCI
    and RFCI in high dimensions. See Pal, Ghosh, and Yang (2025)
    <doi:10.48550/arXiv.2507.00173> for the underlying theory.