Applies sequential clustering algorithm to animal location data 
    based on user-defined parameters. Plots interactive cluster maps and 
    provides a summary dataframe with attributes for each cluster commonly
    used as covariates in subsequent modeling efforts. Additional functions
    provide individual keyhole markup language plots for quick assessment,
    and export of global positioning system exchange format files for
    navigation purposes. 
    Methods can be found at <doi:10.1111/2041-210X.13572>.

Gaussian processes are flexible distributions to model functional data. Whilst
    theoretically appealing, they are computationally cumbersome except for small datasets.
    This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a
    sparse approximation of the likelihood that is generally applicable and, second, an exact method
    for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility
    functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface.
    References: Hoffmann and Onnela (2022) <arXiv:2301.08836>.

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.