Create and manipulate hypergraph objects. This early version of
    rhype allows for the output of matrices associated with the hypergraphs 
    themselves. It also uses these matrices to calculate hypergraph spectra and
    perform spectral comparison. Functionality coming soon includes calculation
    of hyperpaths and hypergraph centrality measures.

Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.

Computes attributable effects based confidence interval, permutation test confidence interval, or asymptotic confidence interval for the average treatment effect on a binary outcome. Methods outlined in further detail in Rigdon and Hudgens (2015) <doi:10.1002/sim.6384>.

Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI,
             nrrd and npy (numpy array) file formats.
             RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and
             geometry-based statistics. Almost all calculations are done using vectorized formulas to
             optimize run speeds. Calculation of several thousands of parameters only takes minutes
             on a single core of a conventional PC. Detailed methodology has been published: Kolossvary
             et al. Circ: Cardiovascular Imaging. 2017;10(12):e006843 <doi: 10.1161/CIRCIMAGING.117.006843>.

Implements the empirical test introduced by Yu, Ge, and Elwert
    (2026) for detecting when randomized interventional analogues should not be
    interpreted as natural mediation effects. The package estimates natural
    effects, their randomized interventional analogues, and TE - TE^R, the
    difference between the total effect and its randomized interventional
    analogue. Rejecting TE - TE^R = 0 falsifies the composite null that the natural
    indirect and direct effects equal their randomized interventional analogues.
    The procedure remains valid in settings where the natural effects themselves
    are not identified, and |TE - TE^R| provides a lower bound on the total
    divergence between the natural and randomized interventional decompositions.