Fast, numerically-stable Gauss-Hermite quadrature rules and
    utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A.
    (1994) <doi:10.2307/2337136> for a reference on these methods.

Two 'Gray Level Co-occurrence Matrix' ('GLCM') implementations are included: The first is a fast 'GLCM' feature texture computation based on 'Python' 'Numpy' arrays ('Github' Repository, <https://github.com/tzm030329/GLCM>). The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, <doi:10.1109/ICIP.2019.8803652>.

Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares 
    implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard
    against convergence issues.

Implements fast, scalable optimization algorithms for
    fitting generalized principal components analysis (GLM-PCA) models,
    as described in "A Generalization of Principal Components
    Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE
    (2002, ISBN:9780262271738), and subsequently "Feature Selection 
    and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial
    Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019)
    <doi:10.1186/s13059-019-1861-6>.

Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).