On Mar 5, 2015 5:01 PM, "Etienne Lord" wrote:
>
> Thanks for the quick reply.
>
> Adding all the dependencies of doParallel (foreach, parallel, iterators)
in
> the DESCRIPTION and in import statements in NAMESPACE resolved the
> build_win problems. Don't know why this is required for Windows build
Thanks for the quick reply.
Adding all the dependencies of doParallel (foreach, parallel, iterators) in
the DESCRIPTION and in import statements in NAMESPACE resolved the
build_win problems. Don't know why this is required for Windows build.
Thanks again.
2015-03-05 18:22 GMT-05:00 Duncan Murdoc
On 05/03/2015 6:03 PM, Etienne Lord wrote:
> Hi,
>
> I'm trying to submit my first package which depends on doParallel:
>
> Depends: R (>= 3.0), igraph, doParallel
It's much better to import what you need. If someone calls one of your
functions using :: notation, it will fail, because it won't
On 05/03/2015 6:16 PM, Brian G. Peterson wrote:
> On Thu, 2015-03-05 at 18:03 -0500, Etienne Lord wrote:
>> Hi,
>>
>> I'm trying to submit my first package which depends on doParallel:
>>
>> Depends: R (>= 3.0), igraph, doParallel
>
> add foreach to your Depends. That should resolve the error you
On Thu, 2015-03-05 at 18:03 -0500, Etienne Lord wrote:
> Hi,
>
> I'm trying to submit my first package which depends on doParallel:
>
> Depends: R (>= 3.0), igraph, doParallel
add foreach to your Depends. That should resolve the error you're
seeing.
> Running hadley devtools: devtools::check(
Hi,
I'm trying to submit my first package which depends on doParallel:
Depends: R (>= 3.0), igraph, doParallel
Running hadley devtools: devtools::check() and devtools::release() result
in no problem (no ERROR nor NOTE on Linux, Mac and Windows).
However, when in use the devtools::build_win() co
Oops, such an amateur mistake. Thanks a lot for your quick response.
Regards
TP
On 03/05/2015 06:49 PM, Prof Brian Ripley wrote:
On 05/03/2015 14:55, Tadeáš Palusga wrote:
Hi,
I'm using this mailing list for the first time and I hope this is the
right one. I don't think that the following
See weightedMean() in the matrixStats package. It's optimized for
data type, speed and memory and implemented in native code so it can
avoid some of these intermediate copies. It's a few times faster than
weighted.mean[.default]();
library(matrixStats)
library(microbenchmark)
n <- 5000
x <- samp
On 05/03/2015 14:55, Tadeáš Palusga wrote:
Hi,
I'm using this mailing list for the first time and I hope this is the
right one. I don't think that the following is a bug but it can be a
performance issue.
By my opinion, there is no need to filter by [w != 0] in last sum of
weighted.mean.d
Hi,
I'm using this mailing list for the first time and I hope this is the
right one. I don't think that the following is a bug but it can be a
performance issue.
By my opinion, there is no need to filter by [w != 0] in last sum of
weighted.mean.default method defined in
src/library/stat
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