Provides the standard operations for signal processing on graphs: 
    graph Fourier transform, spectral graph wavelet transform, 
    visualization tools. It also implements a data driven method
    for graph signal denoising/regression, for details see 
    De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. 
    The package also provides an interface to the SuiteSparse Matrix Collection, 
    <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix 
    benchmarks collected from a wide range of applications.

Fits gastric emptying time series from MRI or 'scintigraphic' measurements
   using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with 
   Stan; computes derived parameters such as t50 and AUC.

Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.

Allows user to choose/gate a region on the plot and returns points
    within it.

Estimates statistically significant marker combination values within
        which one immunologically distinctive group (i.e., disease case) is more associated than
        another group (i.e., healthy control), successively, using various combinations (i.e.,
        "gates") of markers to examine features of cells that may be different between
        groups. For a two-group comparison, the 'gateR' package uses the spatial relative risk
        function estimated using the 'sparr' package. Details about the 'sparr' package
        methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details
        about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.
        More information about relative risk functions using kernel density estimation can be
        found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.

Streamlines downloading and cleaning biodiversity data from Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Information Facility (GBIF).