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.

Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.

Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).