# This file created by conda-build 3.21.8
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Sat May 21 17:30:11 2022
# ------------------------------------------------

package:
  name: r-datavisualizations
  version: 1.2.3
source:
  sha256: 2e657944dc670265ca5ac822bd1387280048df2245bbd0a8a8f0ba480b94a1c6
  url:
    - https://cran.r-project.org/src/contrib/Archive/DataVisualizations/DataVisualizations_1.2.3.tar.gz
    - https://cran.r-project.org/src/contrib/DataVisualizations_1.2.3.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40h01f9f87_0
requirements:
  build:
    - m2-bash 4.3.042 5
    - m2-coreutils 8.25 102
    - m2-filesystem 2016.04 4
    - m2-gcc-libs 5.3.0 4
    - m2-gmp 6.1.0 3
    - m2-libbz2 1.0.6 3
    - m2-libiconv 1.14 3
    - m2-libintl 0.19.7 4
    - m2-make 4.1 5
    - m2-msys2-runtime 2.5.0.17080.65c939c 3
    - m2-sed 4.2.2 3
    - m2-zip 3.0 2
    - m2w64-binutils 2.25.1 5
    - m2w64-bzip2 1.0.6 6
    - m2w64-crt-git 5.0.0.4636.2595836 2
    - m2w64-gcc 5.3.0 6
    - m2w64-gcc-ada 5.3.0 6
    - m2w64-gcc-fortran 5.3.0 6
    - m2w64-gcc-libgfortran 5.3.0 6
    - m2w64-gcc-libs 5.3.0 7
    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gcc-objc 5.3.0 6
    - m2w64-gmp 6.1.0 2
    - m2w64-headers-git 5.0.0.4636.c0ad18a 2
    - m2w64-isl 0.16.1 2
    - m2w64-libiconv 1.14 6
    - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
    - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
    - m2w64-make 4.1.2351.a80a8b8 2
    - m2w64-mpc 1.0.3 3
    - m2w64-mpfr 3.1.4 4
    - m2w64-pkg-config 0.29.1 2
    - m2w64-toolchain 5.3.0 7
    - m2w64-toolchain_win-64 2.4.0 0
    - m2w64-tools-git 5.0.0.4592.90b8472 2
    - m2w64-windows-default-manifest 6.4 3
    - m2w64-winpthreads-git 5.0.0.4634.697f757 2
    - m2w64-zlib 1.2.8 10
    - msys2-conda-epoch 20160418 1
  host:
    - _r-mutex 1.0.1 anacondar_1
    - libblas 3.8.0 11_h8933c1f_netlib
    - liblapack 3.8.0 11_h8933c1f_netlib
    - m2w64-bwidget 1.9.10 2
    - m2w64-bzip2 1.0.6 6
    - m2w64-expat 2.1.1 2
    - m2w64-fftw 3.3.4 6
    - m2w64-flac 1.3.1 3
    - m2w64-gcc-libgfortran 5.3.0 6
    - m2w64-gcc-libs 5.3.0 7
    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gettext 0.19.7 2
    - m2w64-gmp 6.1.0 2
    - m2w64-gsl 2.1 2
    - m2w64-icu 58.2 2
    - m2w64-libiconv 1.14 6
    - m2w64-libjpeg-turbo 1.4.2 3
    - m2w64-libogg 1.3.2 3
    - m2w64-libpng 1.6.21 2
    - m2w64-libsndfile 1.0.26 2
    - m2w64-libtiff 4.0.6 2
    - m2w64-libvorbis 1.3.5 2
    - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
    - m2w64-libxml2 2.9.3 3
    - m2w64-mpfr 3.1.4 4
    - m2w64-pcre2 10.34 0
    - m2w64-speex 1.2rc2 3
    - m2w64-speexdsp 1.2rc3 3
    - m2w64-tcl 8.6.5 3
    - m2w64-tk 8.6.5 3
    - m2w64-tktable 2.10 5
    - m2w64-wineditline 2.101 5
    - m2w64-xz 5.2.2 2
    - m2w64-zlib 1.2.8 10
    - msys2-conda-epoch 20160418 1
    - r-backports 1.4.1 r40h6d2157b_0
    - r-base 4.0.5 hddad469_6
    - r-brio 1.1.3 r40h6d2157b_0
    - r-callr 3.7.0 r40hc72bb7e_0
    - r-cli 3.3.0 r40ha856d6a_0
    - r-colorspace 2.0_3 r40h6d2157b_0
    - r-crayon 1.5.1 r40hc72bb7e_0
    - r-desc 1.4.1 r40hc72bb7e_0
    - r-diffobj 0.3.5 r40h6d2157b_0
    - r-digest 0.6.29 r40ha856d6a_0
    - r-ellipsis 0.3.2 r40h6d2157b_0
    - r-evaluate 0.15 r40hc72bb7e_0
    - r-fansi 1.0.3 r40h6d2157b_0
    - r-farver 2.1.0 r40ha856d6a_0
    - r-ggplot2 3.3.6 r40hc72bb7e_0
    - r-glue 1.6.2 r40h6d2157b_0
    - r-gtable 0.3.0 r40hc72bb7e_3
    - r-isoband 0.2.5 r40ha856d6a_0
    - r-jsonlite 1.8.0 r40h6d2157b_0
    - r-labeling 0.4.2 r40hc72bb7e_1
    - r-lattice 0.20_45 r40h6d2157b_0
    - r-lifecycle 1.0.1 r40hc72bb7e_0
    - r-magrittr 2.0.3 r40h6d2157b_0
    - r-mass 7.3_57 r40h6d2157b_0
    - r-matrix 1.4_1 r40hd2e18ed_0
    - r-mgcv 1.8_40 r40hd2e18ed_0
    - r-munsell 0.5.0 r40hc72bb7e_1004
    - r-nlme 3.1_157 r40he816bda_0
    - r-pillar 1.7.0 r40hc72bb7e_0
    - r-pkgconfig 2.0.3 r40hc72bb7e_1
    - r-pkgload 1.2.4 r40ha856d6a_0
    - r-plyr 1.8.7 r40ha856d6a_0
    - r-pracma 2.3.8 r40hc72bb7e_0
    - r-praise 1.0.0 r40hc72bb7e_1005
    - r-processx 3.5.3 r40h6d2157b_0
    - r-ps 1.7.0 r40h6d2157b_0
    - r-r6 2.5.1 r40hc72bb7e_0
    - r-rcolorbrewer 1.1_3 r40h785f33e_0
    - r-rcpp 1.0.8.3 r40ha856d6a_0
    - r-rcpparmadillo 0.11.1.1.0 r40h399f8d5_0
    - r-rematch2 2.1.2 r40hc72bb7e_1
    - r-reshape2 1.4.4 r40ha856d6a_1
    - r-rlang 1.0.2 r40ha856d6a_0
    - r-rprojroot 2.0.3 r40hc72bb7e_0
    - r-rstudioapi 0.13 r40hc72bb7e_0
    - r-scales 1.2.0 r40hc72bb7e_0
    - r-sp 1.4_7 r40h6d2157b_0
    - r-stringi 1.7.6 r40ha856d6a_2
    - r-stringr 1.4.0 r40hc72bb7e_2
    - r-testthat 3.1.4 r40ha856d6a_0
    - r-tibble 3.1.7 r40h6d2157b_0
    - r-utf8 1.2.2 r40h6d2157b_0
    - r-vctrs 0.4.1 r40ha856d6a_0
    - r-viridislite 0.4.0 r40hc72bb7e_0
    - r-waldo 0.4.0 r40hc72bb7e_0
    - r-withr 2.5.0 r40hc72bb7e_0
  run:
    - libblas >=3.8.0,<4.0a0
    - m2w64-gcc-libs
    - r-base >=4.0,<4.1.0a0
    - r-ggplot2
    - r-pracma
    - r-rcpp >=0.12.12
    - r-rcpparmadillo
    - r-reshape2
    - r-sp
test:
  commands:
    - '"%R%" -e "library(''DataVisualizations'')"'
about:
  home: http://www.deepbionics.org
  license: GPL-3.0-only
  license_family: GPL3
  license_file:
    - D:\bld\r-datavisualizations_1653154475630\_h_env/lib/R/share/licenses/GPL-3
  summary: 'Gives access to data visualisation methods that are relevant from the
    data scientist''s point of view. The flagship idea of ''DataVisualizations'' is
    the mirrored density plot (MD-plot) for either classified or non-classified multivariate
    data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions"
    (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms
    the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin
    plot of ggplot2. Furthermore, a collection of various visualization methods for
    univariate data is provided. In the case of exploratory data analysis, ''DataVisualizations''
    makes it possible to inspect the distribution of each feature of a dataset visually
    through a combination of four methods. One of these methods is the Pareto density
    estimation (PDE) of the probability density function (pdf). Additionally, visualizations
    of the distribution of distances using PDE, the scatter-density plot using PDE
    for two variables as well as the Shepard density plot and the Bland-Altman plot
    are presented here. Pertaining to classified high-dimensional data, a number of
    visualizations are described, such as f.ex. the heat map and silhouette plot.
    A political map of the world or Germany can be visualized with the additional
    information defined by a classification of countries or regions. By extending
    the political map further, an uncomplicated function for a Choropleth map can
    be used which is useful for measurements across a geographic area. For categorical
    features, the Pie charts, slope charts and fan plots, improved by the ABC analysis,
    become usable. More detailed explanations are found in the book by Thrun, M.C.:
    "Projection-Based Clustering through Self-Organization and Swarm Intelligence"
    (2018) <DOI:10.1007/978-3-658-20540-9>.'
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
