2018-12-16 22:45:12 +01:00
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# ==================================================================== #
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# TITLE #
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2020-10-08 11:16:03 +02:00
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# Antimicrobial Resistance (AMR) Analysis for R #
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2018-12-16 22:45:12 +01:00
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# #
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2019-01-02 23:24:07 +01:00
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# SOURCE #
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2020-07-08 14:48:06 +02:00
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# https://github.com/msberends/AMR #
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2018-12-16 22:45:12 +01:00
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# #
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# LICENCE #
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2020-12-27 00:30:28 +01:00
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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2020-10-08 11:16:03 +02:00
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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2018-12-16 22:45:12 +01:00
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# #
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2019-01-02 23:24:07 +01:00
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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2020-01-05 17:22:09 +01:00
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
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2018-12-16 22:45:12 +01:00
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# ==================================================================== #
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2018-07-08 22:14:55 +02:00
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context("kurtosis.R")
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test_that("kurtosis works", {
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skip_on_cran()
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expect_equal(kurtosis(example_isolates$age),
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2018-09-24 23:33:29 +02:00
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3.549319,
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tolerance = 0.00001)
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2020-10-04 21:02:16 +02:00
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2019-08-27 16:45:42 +02:00
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expect_equal(unname(kurtosis(data.frame(example_isolates$age))),
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2018-09-24 23:33:29 +02:00
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3.549319,
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tolerance = 0.00001)
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expect_equal(unname(kurtosis(data.frame(example_isolates$age), excess = TRUE)),
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0.549319,
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tolerance = 0.00001)
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2019-08-27 16:45:42 +02:00
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expect_equal(kurtosis(matrix(example_isolates$age)),
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3.549319,
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tolerance = 0.00001)
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expect_equal(kurtosis(matrix(example_isolates$age), excess = TRUE),
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0.549319,
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tolerance = 0.00001)
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})
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