| 1 | # -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4 |
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| 2 | # $Id$ |
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| 3 | |
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| 4 | PortSystem 1.0 |
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| 5 | PortGroup github 1.0 |
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| 6 | PortGroup python 1.0 |
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| 7 | |
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| 8 | github.setup tazzben EGSimulation 1.1 |
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| 9 | github.project EconScripts |
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| 10 | categories science |
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| 11 | platforms darwin |
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| 12 | maintainers ad.wsu.edu:tazz_ben |
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| 13 | license public-domain |
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| 14 | supported_archs noarch |
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| 15 | |
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| 16 | description Simulate the Ellison & Glaeser statistic using randomness alone |
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| 17 | |
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| 18 | long_description By using a simulation of firm sizes (using a lognormal distribution) and specified geographic regions, standard deviations and employee head count, we can compute the critical regions for the Ellison & Glaeser statistic. In the process, it also calculates herfindahl values and provides critical regions. You can also use Françoise Maurel and Béatrice Sédillot (1999)'s specification for both G and gamma. |
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| 19 | |
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| 20 | homepage ${github.homepage}/tree/master/Simulations/Python/EG%20Statistic |
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| 21 | |
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| 22 | checksums rmd160 04ccfd6aef32e323145116909ce5a89eec76018c \ |
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| 23 | sha256 6ee0bac77adaf1653a07b2ec6cafd53a2c1c5d19bb1092644e1fdee4094fb17b |
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| 24 | |
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| 25 | python.versions 27 |
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| 26 | |
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| 27 | depends_lib-append port:py${python.version}-crypto \ |
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| 28 | port:py${python.version}-numpy \ |
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| 29 | port:py${python.version}-scipy |
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| 30 | |
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| 31 | python.link_binaries_suffix |
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| 32 | |
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| 33 | livecheck.type regex |
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| 34 | livecheck.url ${github.homepage}/tags |
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| 35 | livecheck.regex (\[0-9.\]+)${extract.suffix} |
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