A series of shortcuts for routine tasks originally developed by
    Rafael A. Irizarry to facilitate data exploration.

A cross-validated minimal-optimal feature selection algorithm.
 It utilises popularity counting, hierarchical clustering with feature dissimilarity measures,
 and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). 
              It calculates the final sorted rankings based on a decision matrix where rows represent alternatives 
              and columns represent criteria. The method uses:
              - A numeric vector of weights for each criterion (the sum of weights must be 1).
              - A numeric vector of ideal values for each criterion.
              - A numeric vector of anti-ideal values for each criterion.
              - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, 
                and the extent to which the anti-ideal value is considered worse.
              The function standardizes the decision matrix, normalizes the data, applies weights, and returns the 
              final sorted rankings.

Functions for calculating life history metrics using matrix
    population models ('MPMs'). Described in Jones et al. (2021)
    <doi:10.1101/2021.04.26.441330>.

Enables Retrieval-Augmented Generation (RAG) workflows in R by combining
    local vector search using 'DuckDB' with optional web search via the 'Tavily' API.
    Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW'
    (Hierarchical Navigable Small World) indexing, and modular large language model
    (LLM) invocation. Designed for advanced question-answering, chat-based
    applications, and production-ready AI pipelines. This package is the R
    equivalent of the 'python' package 'RAGFlowChain' available at
    <https://pypi.org/project/RAGFlowChain/>.