Automated cell type annotation for single-cell RNA sequencing data 
    using consensus predictions from multiple large language models (LLMs). LLMs are 
    artificial intelligence models trained on vast text corpora to understand and 
    generate human-like text. This package integrates with 'Seurat' objects and 
    provides uncertainty quantification for annotations. Supports various LLM 
    providers including 'OpenAI', 'Anthropic', and 'Google'. The package leverages 
    these models through their respective APIs (Application Programming Interfaces) 
    <https://platform.openai.com/docs>, <https://docs.anthropic.com/>, and 
    <https://ai.google.dev/gemini-api/docs>.
    For details see Yang et al. (2025) <doi:10.1101/2025.04.10.647852>.

Enhances 'mlexperiments'
    <https://CRAN.R-project.org/package=mlexperiments> with additional
    machine learning ('ML') learners. The package provides R6-based
    learners for the following algorithms: 'glmnet'
    <https://CRAN.R-project.org/package=glmnet>, 'ranger'
    <https://CRAN.R-project.org/package=ranger>, 'xgboost'
    <https://CRAN.R-project.org/package=xgboost>, and 'lightgbm'
    <https://CRAN.R-project.org/package=lightgbm>. These can be used
    directly with the 'mlexperiments' R package.

Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.

An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008)
    <doi:10.1287/opre.1070.0496>, Heinrich (1998) <doi:10.1006/jcom.1998.0471>,
    for R.  This package builds on the original 'Matlab' and 'C++' implementations
    by Mike Giles to provide a full MLMC driver and example level samplers.
    Multi-core parallel sampling of levels is provided built-in.

A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.