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bump version to 3.9.4
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japhir committed May 29, 2024
1 parent 699aea4 commit 3217c35
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4 changes: 2 additions & 2 deletions CITATION.cff
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Expand Up @@ -44,8 +44,8 @@ keywords:
- earth sciences
- marine sciences
license: GPL-3.0-or-later
version: 3.9.3
date-released: '2024-04-24'
version: 3.9.4
date-released: '2024-05-28'
references:
- authors:
- family-names: Zeebe
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2 changes: 1 addition & 1 deletion DESCRIPTION
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@@ -1,6 +1,6 @@
Package: snvecR
Title: Calculate Earth’s Obliquity and Precession in the Past
Version: 3.9.3.9000
Version: 3.9.4
Authors@R:
c(person("Ilja", "Kocken", , "ikocken@hawaii.edu", role = c("aut", "cre", "trl", "cph"),
comment = c(ORCID = "0000-0003-2196-8718")),
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2 changes: 1 addition & 1 deletion NEWS.md
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@@ -1,4 +1,4 @@
# snvecR (development version)
# snvecR 3.9.4

# snvecR 3.9.3
* Added a package option for the cache directory.
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2 changes: 1 addition & 1 deletion inst/CITATION
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Expand Up @@ -11,7 +11,7 @@ bibentry(
## journal = "CRAN / JOSS??",
year = "2023",
abstract = "Easily calculate precession and obliquity from an astronomical solution (defaults to ZB18a from Zeebe and Lourens (2019)) and assumed or reconstructed values for tidal dissipation (Td) and dynamical ellipticity (Ed). This is a translation and adaptation of the 'C'-code in the supplementary material to Zeebe and Lourens (2022), with further details on the methodology described in Zeebe (2022). The name of the 'C'-routine is 'snvec', which refers to the key units of computation: spin vector s and orbit normal vector n.",
version = "3.9.3",
version = "3.9.4",
## volume = {n/a},
## number = {n/a},
## pages = {},
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2 changes: 1 addition & 1 deletion tests/testthat/test-get_solution.R
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test_that("get_solution() inputs are checked", {
pth <- withr::local_tempdir(pattern = "snvecR")
withr::local_options(list(snvecR.cachedir = pth, width = 150))
withr::local_options(list(snvecR.cachedir = pth))
expect_error(get_solution(astronomical_solution = "hoi"))
expect_error(get_solution(astronomical_solution = "full-La11"))
expect_error(get_solution(astronomical_solution = data.frame(x = 1, y = 5))) # this runs via prepare_solution
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