Mark,
I used
if (getRversion()>="3.6.0") RNGkind(sample.kind="Rounding")
And that works. Actually, using rnorm afterwards also yields the same random
numbers.
My question arose from the fact that I confused myself about the noLD output I
was supposed to reproduce. Therefore, my problem
I was dealing with a similar issue but in the context of getting the same unit
test code to work on multiple versions of R in a Travis-CI build. It seems
RNGkind(sample.kind="Rounding”) does not work prior to version 3.6 so I
resorted to using version dependent construction of the argument list
On 09/05/2019 12:43 p.m., Ulrike Grömping wrote:
Hmmmh, but does that also apply if the sample.kind has been set to the
old version? I.e., would
if (getRversion()>="3.6.0") RNGkind(sample.kind="Rounding")
val <- 10
set.seed(val)
discard <- sample(1000, 100)
rnorm(36)
produce the same normal
Hmmmh, but does that also apply if the sample.kind has been set to the
old version? I.e., would
if (getRversion()>="3.6.0") RNGkind(sample.kind="Rounding")
val <- 10
set.seed(val)
discard <- sample(1000, 100)
rnorm(36)
produce the same normal random numbers in 3.5.3 and 3.6.0? I would have
On 09/05/2019 9:15 a.m., Ulrike Grömping wrote:
Dear R package authors,
I am currently struggling with differences in test results between R
versions 3.5.3 and 3.6.0: There are expected ones (from the new behavior
of sample(), which can be switched off by
RNGkind(sample.kind="Rounding")) and
Dear R package authors,
I am currently struggling with differences in test results between R
versions 3.5.3 and 3.6.0: There are expected ones (from the new behavior
of sample(), which can be switched off by
RNGkind(sample.kind="Rounding")) and unexpected ones: The normal random
numbers from
Nope:
```
> meta_cache_update()
ℹ Checking for package metadata updates
✔ Downloaded metadata files, 1.37 MB in 5 files.
✔ Updating metadata database
Good day,
Some programming languages, such as C++, allow function overloading. Is it
possible to mimic it when creating S4 methods?
An example from an undergraduate text book is:
int largerInt(int x, int y);
char largerChar(char first, char second);
You can write the previous function
You can try `pr$draw_tree()`, but it is quite buggy, and fails for
infinite (Suggests) loops:
https://github.com/r-lib/pkgdepends/issues/129
This might be an easier way to get the direct dependencies of each
package in the
dependency tree:
tibble::as_tibble(pr$get_install_plan(FALSE))[,
--- Begin Message ---
Could https://github.com/jimhester/itdepends help?
Maëlle.
Den torsdag 9 maj 2019 12:10:14 CEST, Rainer M Krug skrev:
Thanks Gabor.
This gives me an impressive long list. Is there a way to see this graphically?
Parsing seems to be a long, tedious process.
I
Thanks Gabor.
This gives me an impressive long list. Is there a way to see this graphically?
Parsing seems to be a long, tedious process.
I want to identify packages which I should leave out to reduce the number of
(indirect) dependencies.
Rainer
> On 9 May 2019, at 11:24, Gábor Csárdi
You can do something like this with https://github.com/r-lib/pkgdepends:
pr <- pkgdepends::remotes()$new("local::.", library = tempfile())
#> ℹ Creating library directory:
`/var/folders/59/0gkmw1yj2w7bf2dfc3jznv5wgn/T//Rtmpo5NL9R/filee95427c73f55`
pr$solve()
#> ℹ Checking for package
Please resubmit, we do not have the submission anymore, so I guess it
has been auto archived and either soemthing went wrong with the auto
generated message or it went into your spam folder.
It never takes 50 days ...
Best,
Uwe Ligges
On 09.05.2019 09:43, julien chiquet wrote:
Hi,
I
Hi
I am quite sure, there was a recent discussion about dependency graphs of
packages. I saw the post from Dirk at
http://dirk.eddelbuettel.com/blog/2018/02/28/#017_dependencies but
`tools::package_dependencies()` does only work on packages on CRAN.
But my package is not yet on CRAN (but on
Hi,
I have submitted a package to CRAN (resubmission of a new submission) more
than 50 days ago and still have no feedback from CRAN (see PLNmodels)
https://cransays.itsalocke.com/articles/dashboard.html
I know that the workload of CRAN volunteers is extremely heavy and really
want to thank
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