A convenient interface with the OpenAI 'ChatGPT' API <https://openai.com/api>. 'gptr' allows you to interact with 'ChatGPT', a powerful language model, for various natural language processing tasks. The 'gptr' R package makes talking to 'ChatGPT' in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With 'gptr', you can use 'ChatGPT' in R without any hassle, making it simpler for everyone to do cool things with language!

We implement and extend the Dividing Local Gaussian Process 
    algorithm by Lederer et al. (2020) <doi:10.48550/arXiv.2006.09446>. Its
    main use case is in online learning where it is used to train a network of
    local GPs (referred to as tree) by cleverly partitioning the input space. 
    In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of
    data. The package includes methods to create the tree and set its
    parameter, incorporating data points from a data stream as well as making
    joint predictions based on all relevant local GPs.

Large language models are readily accessible via API. This
    package lowers the barrier to use the API inside of your development
    environment.  For more on the API, see
    <https://platform.openai.com/docs/introduction>.

An R interface to the 'GPTZero' API (<https://gptzero.me/docs>). Allows 
    users to classify text into human and computer written with probabilities. Formats
    the data into data frames where each sentence is an observation. Paragraph-level
    and document-level predictions are organized to align with the sentences.

GPUs are great resources for data analysis, especially in statistics and linear algebra. Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the 'GPUmatrix' package (available on CRAN). 'GPUmatrix' mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. 'GPUmatrix' relies on either the 'Torch' or 'Tensorflow' R packages to perform the GPU operations. We have demonstrated its usefulness for several statistical applications and machine learning applications: non-negative matrix factorization, logistic regression and general linear models. We have also included a comparison of GPU and CPU performance on different matrix operations.