Provides 'R' bindings to the 'GGML' tensor library for machine
    learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU
    fallback. The package features a 'Keras'-like sequential API and a
    'PyTorch'-style 'autograd' engine for building, training, and deploying
    neural networks. Key capabilities include high-performance 5D tensor
    operations, 'f16' precision, and efficient quantization. It supports
    native 'ONNX' model import (50+ operators) and 'GGUF' weight loading
    from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for
    zero-overhead inference via dedicated weight buffering, it integrates
    seamlessly as a 'parsnip' engine for 'tidymodels' and provides
    first-class learners for the 'mlr3' framework.
    See <https://github.com/ggml-org/ggml> for more information about the
    underlying library.

Estimate Gaussian graphical models with nonconvex penalties,
  including methods described by Williams (2020) <doi:10.31234/osf.io/ad57p>.
  Penalties include atan (Wang and Zhu, 2016) <doi:10.1155/2016/6495417>,
  seamless L0 (Dicker, Huang and Lin, 2013) <doi:10.5705/ss.2011.074>,
  exponential (Wang, Fan and Zhu, 2018) <doi:10.1007/s10463-016-0588-3>,
  smooth integration of counting and absolute deviation (Lv and Fan, 2009)
  <doi:10.1214/09-AOS683>, logarithm (Mazumder, Friedman and Hastie, 2011)
  <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation
  (Fan and Li, 2001) <doi:10.1198/016214501753382273>, and minimax concave
  penalty (Zhang, 2010) <doi:10.1214/09-AOS729>. The package also provides
  extensions for variable inclusion probabilities, multiple regression
  coefficients, and statistical inference (Janková and van de Geer, 2015)
  <doi:10.1214/15-EJS1031>.

Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.