Generate Music Easily and Show Them Anywhere
Provides a simple and intuitive high-level language, with which
you can create music easily. Takes care of all the dirty technical
details in converting your music to musical scores and audio files.
Works in 'R Markdown' documents <https://rmarkdown.rstudio.com/>,
R 'Jupyter Notebooks' <https://jupyter.org/>, and 'RStudio'
<https://www.rstudio.com/>, so you can embed generated music
anywhere. Internally, uses 'MusicXML' <https://www.musicxml.com/> to
represent musical scores, and 'MuseScore' <https://musescore.org/> to
convert 'MusicXML'.
base64enc, glue, htmltools, knitr, magick, magrittr, MASS,
rlang, rstudioapi, stringr, utils

Accessing a Geometallurgical Database with R
c(person("K. Gerald", "van den Boogaart", role = c("aut", "cre"),
email = "support@boogaart.de"),
person("Stephan", "Matos Camacho", role = c("aut"),
email = "s.matos@hzdr.de")
)
K. Gerald van den Boogaart [aut, cre], Stephan Matos Camacho [aut]
R(>= 2.1.0), DBI, RMySQL, foreach, methods, digest, shiny
A template for a geometallurgical database and a fast and easy
interface for accessing it is provided in this package.
(C) 2016 by Helmholtz Institute Freiberg for Resource
Technology

c(person(given = "Raimon",
family = "Tolosana-Delgado",
role = c("aut"),
email = "r.tolosana@hzdr.de",
comment = c(ORCID = "0000-0001-9847-0462")),
person(given = "Ute",
family = "Mueller",
role = c("aut"),
email = "u.mueller@ecu.edu.au"),
person(given = "K. Gerald",
family = "van den Boogaart",
role = c("ctb", "cre"),
email = "support@boogaart.de"),
person(given = "Hassan",
family = "Talebi",
role = c("ctb", "cph"),
email = "hassan.talebi@csiro.au"),
person(given = "Helmholtz-Zentrum Dresden-Rossendorf", role = "cph"),
person(given = "Edith Cowan University", role = "cph")
)
FNN, JADE, jointDiag, DescTools, knitr, rmarkdown (>= 2.3),
magrittr, readxl
methods, gstat, compositions (>= 2.0), sp, boot, foreach,
utils, RColorBrewer
Support for geostatistical analysis of multivariate data,
in particular data with restrictions, e.g. positive amounts data,
compositional data, distributional data, microstructural data, etc.
It includes descriptive analysis and modelling for such data, both
from a two-point Gaussian perspective and multipoint perspective.
The methods mainly follow Tolosana-Delgado, Mueller and van den
Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
(C) 2020 by Helmholtz-Zentrum Dresden-Rossendorf and Edith
Cowan University; gsi.DS code by Hassan Talebi
'Anamorphosis.R' 'compositionsCompatibility.R'
'gstatCompatibility.R' 'variograms.R' 'gmValidationStrategy.R'
'gmAnisotropy.R' 'abstractClasses.R' 'accuracy.R' 'data.R'
'exploratools.R' 'genDiag.R' 'geostats.R' 'gmDataFrameStack.R'
'gmSimulation.R' 'gmSpatialMethodParameters.R'
'gmSpatialModel.R' 'grids.R' 'mask.R' 'spSupport.R'
'uncorrelationTest.R' 'zzz.R'
Raimon Tolosana-Delgado [aut] (<https://orcid.org/0000-0001-9847-0462>),
Ute Mueller [aut],
K. Gerald van den Boogaart [ctb, cre],
Hassan Talebi [ctb, cph],
Helmholtz-Zentrum Dresden-Rossendorf [cph],
Edith Cowan University [cph]