python >= 2.6, by default rasciidoc uses a system
installation of asciidoc. If a system installation of asciidoc
is not available, it downloads the sources from
(<http://gitlab.com/asciidoc>). GNU source-highlight is
recommended.

document, git2r, highr, knitr, reticulate, xfun

checkmate, document, git2r, highr, knitr, reticulate, xfun
devtools, pkgload, rasciidoc, rmarkdown, rprojroot, RUnit,
testthat, withr

checkmate, document, fritools, gert, highr, knitr, reticulate,
xfun

checkmate, document, fritools (>= 1.2.0), gert, highr, knitr,
reticulate, xfun

checkmate, document (>= 3.3.0), fritools (>= 1.2.0), gert,
highr, knitr, reticulate, tools

checkmate, document (>= 3.3.0), fritools (>= 1.3.0), gert,
highr, knitr, reticulate, tools

Inspired by Karl Broman`s reader on using 'knitr'
with 'asciidoc'
(<https://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is
merely a wrapper to 'knitr' and 'asciidoc'.

Daniel Wiesmann <daniel.wiesmann@ist.utl.pt> and David Quinn
<djq@mit.edu>
This package contains functions to perform supervised and
pixel based raster image classification. It can be used for
land-cover analysis in a straightforward way. Two
classification algorithms are implemented: a classifier using
Multinomial Logistic Regression and a Maximum Likelihood
Classifier (MLC). The output includes the classified raster and
standard classification accuracy assessment.

Daniel Wiesmann <daniel.wiesmann@ist.utl.pt> and David Quinn <djq@mit.edu>
This package contains functions to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment (accuracy matrix, overall accuracy and kappa coefficient). An option for in-sample verification is available.
methods, car, nnet, RSNNS, e1071, randomForest