# This file created by conda-build 3.21.8
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Thu Feb 24 08:17:38 2022
# ------------------------------------------------

package:
  name: r-spatstat
  version: '2.3_3'
source:
  sha256: 1906b53ae9aad318159a6ea7f0db165636bc70dc6c4a33db7e8810eece19736a
  url:
    - https://cran.r-project.org/src/contrib/Archive/spatstat/spatstat_2.3-3.tar.gz
    - https://cran.r-project.org/src/contrib/spatstat_2.3-3.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40h6addd8b_0
requirements:
  build:
    - m2-gcc-libs 5.3.0 4
    - m2-libbz2 1.0.6 3
    - m2-msys2-runtime 2.5.0.17080.65c939c 3
    - m2-zip 3.0 2
    - msys2-conda-epoch 20160418 1
  host:
    - _r-mutex 1.0.1 anacondar_1
    - intel-openmp 2022.0.0 h57928b3_3663
    - libblas 3.9.0 13_win64_mkl
    - liblapack 3.9.0 13_win64_mkl
    - 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-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
    - mkl 2022.0.0 h0e2418a_796
    - msys2-conda-epoch 20160418 1
    - r-abind 1.4_5 r40hc72bb7e_1003
    - r-base 4.0.5 hddad469_4
    - r-deldir 1.0_6 r40he816bda_0
    - r-goftest 1.2_3 r40h6d2157b_0
    - r-lattice 0.20_45 r40h6d2157b_0
    - r-matrix 1.4_0 r40hd2e18ed_0
    - r-mgcv 1.8_38 r40hd2e18ed_0
    - r-nlme 3.1_155 r40he816bda_0
    - r-polyclip 1.10_0 r40ha856d6a_2
    - r-rpart 4.1.16 r40h6d2157b_0
    - r-spatstat.core 2.4_0 r40ha856d6a_0
    - r-spatstat.data 2.1_2 r40hc72bb7e_0
    - r-spatstat.geom 2.3_2 r40h6d2157b_0
    - r-spatstat.linnet 2.3_2 r40h6d2157b_0
    - r-spatstat.random 2.1_0 r40ha856d6a_0
    - r-spatstat.sparse 2.1_0 r40h6d2157b_0
    - r-spatstat.utils 2.3_0 r40hcd874cb_0
    - r-tensor 1.5 r40hc72bb7e_1003
    - tbb 2021.5.0 h2d74725_0
    - ucrt 10.0.20348.0 h57928b3_0
    - vc 14.2 hb210afc_6
    - vs2015_runtime 14.29.30037 h902a5da_6
  run:
    - r-base >=4.0,<4.1.0a0
    - r-spatstat.core >=2.4_0
    - r-spatstat.data >=2.1_2
    - r-spatstat.geom >=2.3_2
    - r-spatstat.linnet >=2.3_2
    - r-spatstat.random >=2.1_0
    - r-spatstat.utils >=2.3_0
test:
  commands:
    - '"%R%" -e "library(''spatstat'')"'
about:
  home: http://www.spatstat.org
  license: GPL-2.0-or-later
  license_family: GPL2
  license_file:
    - D:\bld\r-spatstat_1645691003883\_h_env/lib/R/share/licenses/GPL-2
  summary: Comprehensive open-source toolbox for analysing Spatial Point Patterns.
    Focused mainly on two-dimensional point patterns, including multitype/marked points,
    in any spatial region. Also supports three-dimensional point patterns, space-time
    point patterns in any number of dimensions, point patterns on a linear network,
    and patterns of other geometrical objects. Supports spatial covariate data such
    as pixel images. Contains over 2000 functions for plotting spatial data, exploratory
    data analysis, model-fitting, simulation, spatial sampling, model diagnostics,
    and formal inference. Data types include point patterns, line segment patterns,
    spatial windows, pixel images, tessellations, and linear networks. Exploratory
    methods include quadrat counts, K-functions and their simulation envelopes, nearest
    neighbour distance and empty space statistics, Fry plots, pair correlation function,
    kernel smoothed intensity, relative risk estimation with cross-validated bandwidth
    selection, mark correlation functions, segregation indices, mark dependence diagnostics,
    and kernel estimates of covariate effects. Formal hypothesis tests of random pattern
    (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton,
    two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson,
    Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted
    to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar
    to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott
    cluster processes, and determinantal point processes. Models may involve dependence
    on covariates, inter-point interaction, cluster formation and dependence on marks.
    Models are fitted by maximum likelihood, logistic regression, minimum contrast,
    and composite likelihood methods. A model can be fitted to a list of point patterns
    (replicated point pattern data) using the function mppm(). The model can include
    random effects and fixed effects depending on the experimental design, in addition
    to all the features listed above. Fitted point process models can be simulated,
    automatically. Formal hypothesis tests of a fitted model are supported (likelihood
    ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for
    model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating
    the fitted model include simulation envelopes, residuals, residual plots and Q-Q
    plots, leverage and influence diagnostics, partial residuals, and added variable
    plots.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
    - ocefpaf
