Implements an algorithm for Latent Dirichlet
    Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
    using style conventions from the 'tidyverse',
    Wickham et al. (2019)<doi:10.21105/joss.01686>,
    and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
    Fitting is done via collapsed Gibbs sampling.
    Also implements several novel features for LDA such as guided models and
    transfer learning.

Provides a unified tidyverse-compatible interface to R's machine
    learning ecosystem - from data ingestion to model publishing. The tl_read()
    family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases
    ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3',
    'GitHub', 'Kaggle'). The tl_model() function wraps established
    implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart',
    'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function
    signatures and tidy tibble output. Results flow into unified 'ggplot2'-based
    visualization and optional formatted 'gt' tables via the tl_table() family.
    The underlying algorithms are unchanged; 'tidylearn' simply makes them
    easier to use together. Access raw model objects via the $fit slot for
    package-specific functionality.
    Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>,
    LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
    support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>,
    and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.

A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'OpenAI', 'Gemini', 'Mistral', and local models via 'Ollama' into R workflows. The package supports text, image, audio, video, and document interactions; a unified media interface for attaching inline files or uploading to provider file stores; batch request APIs for cost-efficient large-scale processing; and a pipeline-oriented interface for seamless integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://aistudio.google.com/>, <https://mistral.ai/> and <https://ollama.com>.