Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'.
    Currently provides Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA),
    Weighted Enrichment Analysis for ORA and GSEA, Network-based Set Enrichment Analysis (NSEA),
    multi-layer network-based enrichment, and multi-omics integration workflows. Additional
    features include early fusion at the feature level, late fusion at the pathway level,
    multi-omics contribution tracing, topology-aware explanation helpers, Bayesian term
    selection, and extremely fast Random Walk with Restart (RWR) using 'RcppEigen'. The
    enrichment methods build on GSEA by Subramanian et al. (2005)
    <doi:10.1073/pnas.0506580102>, the multilevel strategy derived from 'fgsea'
    by Korotkevich et al. (2021) <doi:10.1101/060012>, and network-based
    enrichment ideas described by Glaab et al. (2012)
    <doi:10.1093/bioinformatics/bts389>.

Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See <https://maayanlab.cloud/Enrichr/> for further details.

Provides the "enrich()" method for augmenting list-like R objects with additional, model-specific components. Methods are currently available for objects of class "family", "link-glm", "lm", "glm", and "betareg". Enriched objects retain their original class and remain compatible with existing methods. For example, enriching a "glm" object produces an "enriched_glm" object that also inherits from "glm". In addition to the standard components, the "enriched_glm" object includes methods for simulation and functions to compute scores, observed and expected information matrices, first-order bias, and other model quantities such as densities, probabilities, and quantiles, which can be evaluated at use-supplied parameter values. The package also provides tools for generating customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.