Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.

Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes polygon vector or data frame objects, and is much faster. 
   There is support for the main options provided by the rasterize() function, 
   including setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.

Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2023) <doi:10.1101/2023.11.06.565896>) for fitting functional mixed models.

Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise.  See the reference: Mengyang Gu and Yanxun Xu, 2020, Journal of Computational and Graphical Statistics.

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>.