Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

Probability propagation in graphical independence networks, also
    known as Bayesian networks or probabilistic expert systems. Documentation
    of the package is provided in vignettes included in the package and in
    the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>).
    See 'citation("gRain")' for details.

Given a landscape resistance surface, creates minimum planar graph
    (Fall et al. (2007) <doi:10.1007/s10021-007-9038-7>) and grains of connectivity
    (Galpern et al. (2012) <doi:10.1111/j.1365-294X.2012.05677.x>) models that can be
    used to calculate effective distances for landscape connectivity at multiple scales.
    Documentation is provided by several vignettes, and a paper
    (Chubaty, Galpern & Doctolero (2020) <doi:10.1111/2041-210X.13350>).

A native R implementation of grammatical evolution (GE).
    GE facilitates the discovery of programs that can achieve a desired goal.
    This is done by performing an evolutionary optimisation over a population
    of R expressions generated via a user-defined context-free grammar (CFG)
    and cost function.

Numerical integration with Gram polynomials (based on <arXiv:2106.14875> [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]).

Interactively applies the Guidelines for Reporting About Network Data (GRAND) to an 'igraph' object, and generates a uniform narrative or tabular description of the object.

Nucleotide conversion sequencing experiments have been
  developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such 
  experiments require specialized tools for primary processing such as GRAND-SLAM,
  (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for 
  downstream analyses. 'grandR' provides a comprehensive toolbox for quality control,
  kinetic modeling, differential gene expression analysis and visualization of such data.