Provides GPU enabled functions for 'R' objects in a simple and
    approachable manner.  New 'gpu*' and 'vcl*' classes have been provided to
    wrap typical 'R' objects (e.g. vector, matrix), in both host and device
    spaces, to mirror typical 'R' syntax without the need to know 'OpenCL'.

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.

Process open standard GPX files into data.frames for further use and analysis in R.

Server implementation of 'GraphQL' <http://graphql.github.io/graphql-spec/>,
    a query language originally created by Facebook for describing data requirements on complex application
    data models.  Visit <http://graphql.org> to learn more about 'GraphQL'.

Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.

Provides a comprehensive suite of genome-wide association study (GWAS) methods
    specifically designed for biobank-scale data. The package offers computationally efficient
    and robust association tests for time-to-event traits (e.g., Bi et al., 2020 
    <doi:10.1016/j.ajhg.2020.06.003>), ordinal categorical traits (e.g., Bi et al., 2021 
    <doi:10.1016/j.ajhg.2021.03.019>), and longitudinal traits (Xu et al., 2025 
    <doi:10.1038/s41467-025-56669-1>). Additionally, it includes functions for 
    simulating genotype and phenotype data to support research and method development.