search for: simpify

Spotted by coccinelle: drivers/gpu/drm/nouveau/core/subdev/fuse/gm107.c:50:5-8: WARNING: end returns can be simpified Signed-off-by: Martin Peres <martin.peres at free.fr> --- drm/nouveau/nvkm/subdev/fuse/gm107.c | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/drm/nouveau/nvkm/subdev/fuse/gm107.c b/drm/nouveau/nvkm/subdev/fuse/gm107.c index ba19158..0b256aa 100644

There is either something wrong with either me or is Yacas/Ryacas doing odd things. See below I ask to simpify an expression and the there's output! If this is a simplification.. well.. Do you think there is something set wrong somewhere. Thanks. > library(Ryacas) Loading required package: XML > a=Sym("a") > N=Sym("N") > b=Sym("b") > E=Sym("E") &gt...

I'm having difficulty getting rid of extra whitespace above and below an EPS figure. For example, if I generate an output file using these commands: > R.test(output='none') where: R.test <- function(output=NULL) { if (output == 'yes') { ps.options(pointsize=10) postscript('plot.eps', paper='special', horizontal=FALSE, onefile=FALSE,

Hi list, I am probably overlooking something, but can not figure out what is going on nor can I find something through google. I just converted a Samba 4 PDC to Samba 4 AD DC (using the samba provided tools). I hooked up a Linux laptop to the network configure winbind, joined the domain and am able to login with my credentials from Samba AD. However I can not seem to get it working on the

In an effort to reduce the code, start to use few bits of GLib: - replace the gnulib c-type module - replace the gnulib getprogname module - use g_spawn_sync to launch curl, and drop a file reading function Pino Toscano (6): Include glib.h in p2v.h Use g_ascii_isspace instead of c_isspace from gnulib Use g_get_prgname instead of getprogname from gnulib build: remove no more used gnulib

I'm interested in analysing some of my data using multinomial regression. I have been using nnet's multinom so far. However, I found that some of the data shows overdispersion and hence want to change to robust multinomial regression, package: multinomRob I have succesfully implemented Agresti's (2002) alligator example (chapter 7, p 268) using the option MLEonly=TRUE as there are too