person("Jacob", "Kaplan", email = "jacobkap@sas.upenn.edu", role = c("aut", "cre")),
person("Benjamin", "Schlegel", email = "kontakt@benjaminschlegl.ch", role =  "ctb"))
Description: Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. This package provides a significant speed increase from creating dummy variables through model.matrix().

Title: Fast Creation of Dummy (Binary) Columns and Rows from
person("Jacob", "Kaplan", email = "jkkaplan6@gmail.com", role = c("aut", "cre")),
Description: Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().

person("Jacob", "Kaplan", email = "jkkaplan6@gmail.com", role = c("aut", "cre"),
Author: Jacob Kaplan [aut, cre] (<https://orcid.org/0000-0002-0601-0387>),

Suggests: testthat (>= 2.1.0), knitr, rmarkdown, covr, spelling

Title: Fast Rcpp implementation of Gauss-Hermite quadrature
Maintainer: Alexander W Blocker <ablocker@gmail.com>
Description: Fast, numerically-stable Gauss-Hermite quadrature
Packaged: 2011-12-05 15:34:53 UTC; awblocker

Packaged: 2011-12-03 16:51:11 UTC; awblocker

Packaged: 2014-08-14 04:04:21 UTC; awblocker

Title: Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature
Description: 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.

Packaged: 2018-09-29 18:26:45 UTC; awblocker

Authors@R: c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8024-1546")), person("Takahiro", "Toizumi", role = "cph", comment = "Author of the fastGLCM Python code") )
Description: 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>.
SystemRequirements: apt-get-pip: apt-get install -y python3-pip (deb),
python3-pip: python3 -m pip install -U pip (deb), numpy: pip3
install -U numpy (deb), cv2: pip3 install -U opencv-python
(deb), libarmadillo: apt-get install -y libarmadillo-dev (deb),
libblas: apt-get install -y libblas-dev (deb), liblapack:
apt-get install -y liblapack-dev (deb), libarpack++2: apt-get
install -y libarpack++2-dev (deb), gfortran: apt-get install -y
Imports: Rcpp (>= 1.0.8.3), R6, rlang, OpenImageR, utils
Suggests: reticulate, covr, knitr, rmarkdown, testthat (>= 3.0.0)
Takahiro Toizumi [cph] (Author of the fastGLCM Python code)