# This file created by conda-build 3.18.11
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Sun Mar  1 17:15:08 2020
# ------------------------------------------------

package:
    name: r-spatstat
    version: '1.63_3'
source:
    sha256: 07b4a1a1b37c91944f31779dd789598f4a5ad047a3de3e9ec2ca99b9e9565528
    url:
        - https://cran.r-project.org/src/contrib/Archive/spatstat/spatstat_1.63-3.tar.gz
        - https://cran.r-project.org/src/contrib/spatstat_1.63-3.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36h796a38f_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
        - intel-openmp 2020.0 166
        - libblas 3.8.0 15_mkl
        - liblapack 3.8.0 15_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 4
        - m2w64-mpfr 3.1.4 4
        - m2w64-pcre 8.38 2
        - 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 2020.0 166
        - msys2-conda-epoch 20160418 1
        - r-abind 1.4_5 r36h6115d3f_1002
        - r-base 3.6.2 hf0ea6f9_1
        - r-deldir 0.1_25 r36h17ddedb_0
        - r-goftest 1.2_2 r36hda5aaf8_0
        - r-lattice 0.20_40 r36hda5aaf8_0
        - r-matrix 1.2_18 r36h0adb578_2
        - r-mgcv 1.8_31 r36h0adb578_0
        - r-nlme 3.1_144 r36h17ddedb_0
        - r-polyclip 1.10_0 r36h796a38f_1
        - r-rpart 4.1_15 r36hda5aaf8_1
        - r-spatstat.data 1.4_3 r36h6115d3f_0
        - r-spatstat.utils 1.17_0 r36h301d43c_0
        - r-tensor 1.5 r36h6115d3f_1002
    run:
        - m2w64-gcc-libs
        - r-abind
        - r-base >=3.6,<3.7.0a0
        - r-deldir >=0.0_21
        - r-goftest >=1.2_2
        - r-matrix
        - r-mgcv
        - r-nlme
        - r-polyclip >=1.10_0
        - r-rpart
        - r-spatstat.data >=1.4_2
        - r-spatstat.utils >=1.17_0
        - r-tensor
test:
    commands:
        - '"%R%" -e "library(''spatstat'')"'
about:
    home: http://www.spatstat.org
    license: GPL-2
    license_family: GPL2
    license_file:
        - D:\bld\r-spatstat_1583083098380\_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
