Utilities used across packages of the 'rtemis' ecosystem. Includes the msg() 
    messaging system and the fmt() formatting system. Provides a library of 'S7' properties,
    test_* functions that return logical values, check_* functions that throw informative errors, 
    and clean_* functions that return validated and coerced values. This code began as part of the 
    'rtemis' package (<doi:10.32614/CRAN.package.rtemis>).

Unified interface for creating LLM and Agent objects, generating responses and 
    performing batch inference based on a type-checked and validated 'S7' backend. Features 
    reasoning, structured output, memory management, and tool use. Supports 'Ollama' 
    <https://docs.ollama.com/api>, 
    'OpenAI'-compatible <https://developers.openai.com/api/reference/overview>, and 
    'Anthropic'-compatible <https://platform.claude.com/docs/en/api/getting-started> endpoints.

A collection of R Markdown templates for nicely structured, reproducible 
    data analyses in R. The templates have embedded examples on how to write 
    citations, footnotes, equations and use colored message/info boxes, how to 
    cross-reference different parts/sections in the report, provide a nice 
    table of contents (toc) with a References section and proper R session 
    information as well as examples using DT tables and ggplot2 graphs. 
    The bookdown Lite template theme supports code folding.

A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, Tucker, INDSCAL, RESCAL, DEDICOM, PARAFAC2, and 2DLDA. For
    tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hadamard product for a list of matrices.