similar to: [PATCH] Add SM3 secure hash algorithm

Hi, This implementation looks fine, but there is no specification for using SM3 in the SSH protocol. Could I suggest that you start by talking to the IETF to get the standardisation process started? https://mailman3.ietf.org/mailman3/lists/ssh.ietf.org/ is a good mailing list to start at. There have been recent conversations in the IETF about how best to handle national cryptographic standards

I keep getting the 'memory.size' error message when I run a program I have been writing. It always it cannot allocate a vector of a certain size. I believe the error comes in the code fragement below where I have multiple arrays that could be taking up space. Does anyone know a good way around this? w1 <- outer(xk$xk1, data[,x1], function(y,z) abs(z-y)) w2 <- outer(xk$xk2,

Dear R Help, I read through the archives pretty extensively before sending this email, as it seemed there were several threads on using predict with GLM. However, while my issue is similar to previous posts (cannot get it to predict using new data), none of the suggested fixes are working. The important bits of my code: set.seed(644) n0=200 #number of observations

Hi all, I'm working on a target whose registers have equal-sized subregisters and all of those subregisters can be named (or the other way round: registers can be grouped into super registers). So for instance we've got 16 registers W (as in wide) W0..W15 and 32 registers N (as in narrow) N0..N31. This way, W0 is made by grouping N0 and N1, W1 is N2 and N3, W2 is N4 and N5, ..., W15 is

Dear R-Help List, I'm trying to simulate data from a conditional distribution, and haven't been able to modify my existing code to do so. I searched the archives, but didn't find any previous post that matched my question. n=10000 pop = data.frame(W1 = rbinom(n, 1, .2), W2 = runif(n, min = 3, max = 8), W3 = rnorm(n, mean=0, sd=2)) pop = transform(pop, A = rbinom(n, 1,

I have a bit of an issue with rsync. I am using to keep directories in sync via another server for backup. Here is the server config [w1] path = /w1 comment = w1 web dir [w2] path = /w2 comment = w2 web dir Now on the client i run this command rsync -avv --delete --force domain.com::w1/ /w1/ It will NOT delete anything that is no on the server anymore.. for example on the server/client there

Dear R users, I used to "OPTIM" to minimize the obj. function below. Even though I used the true parameter values as initial values, the results are not very good. How could I improve my results? Any suggestion will be greatly appreciated. Regards, Kathryn Lord #------------------------------------------------------------------------------------------ x = c(0.35938587,

Dear R users, I used to "OPTIM" to minimize the obj. function below. Even though I used the true parameter values as initial values, the results are not very good. How could I improve my results? Any suggestion will be greatly appreciated. Regards, Kathryn Lord #------------------------------------------------------------------------------------------ x = c(0.35938587,

Hi Quentin, > It sounds like you only need the earlyclobber description for the N, N > variant. > In other words, as long as you use different opcodes for widen-op NN and > widen-op WN, you model exactly what you want. > > What am I missing? > we are using different opcodes for widen-op NN and widen-op WN. My understanding is that not setting earlyclobber to the W, N

Hello, I'm trying to get percentiles (PERCENTRANK for excel users) by factor in the following data.frame: myExample <- data.frame(Ret=seq(-2, 2.5, by=0.5),PE=seq(10,19),Sectors=rep(c("Financial","Industrial"),5)) myExample <- na.omit(myExample) Thanks to Patrick I I managed to put together the following lines which does it for the "Ret" column: myecdf

Dear R-helpers, I am struggling with an optimization problem at the moment and decided to write the list looking for some help. I will use a very small example to explain what I would like to. Thanks in advance for your help. We would like to distribute resources from 4 warehouses to 3 destinations. The costs associated are as follows: Destination >From 1 2 3 Total

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

Hi, Sorry for the confusion. I would like to estimate a model wherein the marginals of z with respect to w1 and w2 are smooth functions of x and y. I have data on z, x, y, w1 and w2. so E[dz/dw1] = f(x,y) and E[dz/dw2] = g(x,y) and I would like to estimate f(x,y) and g(x,y) I suppose I could try to fit something more general using projection pursuit, but the nature of the problem suggests

Hi all developers, I'm changing compiler from gcc to llvm on a RISCV target now. but I found in some case the assembly code generated by llvm is much more than gcc. It cause my program's performance about 40% decrease. The flowing is a simple test code. It shows the problem. We can see than gcc prefer to use pointer to iterate the array, but llvm perfere to use index to iterate

Dear all, I'm looking to create a formula within a function to pass to glmer() and I'm having a problem that the following example will illustrate: library(lme4) y1 = rnorm(10) x1 = data.frame(x11=rnorm(10), x12=rnorm(10), x13=rnorm(10)) x1 = data.matrix(x1) w1 = data.frame(w11=sample(1:3,10, replace=TRUE), w12=sample(1:3,10, replace=TRUE), w13=sample(1:3,10, replace=TRUE)) test1 <-

Hello: I sent this question the other day with the wrong subject heading and couple typos, with no response. So, here I go again, having made those corrections. I would like to estimate, for lack of a better description, a partially additive non-parametric model with the following structure: z~ f(x,y):w1 + g(x,y):w2 + e In other words, I'd like to estimate the marginals with respect to

Dear R-list! My question is related to an Anova including within and between subject factors and unequal group sizes. Here is a minimal example of what I did: library(car) within1 <- c(1,2,3,4,5,6,4,5,3,2); within2 <- c(3,4,3,4,3,4,3,4,5,4) values <- data.frame(w1 = within1, w2 = within2) values <- as.matrix(values) between <- factor(c(rep(1,4), rep(2,6))) betweenanova <-

Linux: RedHat 7.1 Samba: 2.2.7 Windoze #1: 98SE Windoze #2: W2K Here is the situation: copy files from W1 to Linux. At same time, transfer those files from Linux to W2. Of course, the transfer from Linux to W2 doesn't occur until the particular file has completed the transfer from W1 to Linux. This senerio of dual transfer from the same area on the Linux disc will ultimately case the smbd

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

Full_Name: Raymond Salvador Version: R 1.6.2 OS: Windows ME Submission from: (NULL) (131.111.93.195) The mean function applied on individual components of several correlation matrices gives a wrong result (gives the first value instead of the mean). Here there is a simple example x1 <- rnorm(10,1,1) y1 <- rnorm(10,1,1) z1 <- cbind(x1,y1) w1 <- cor(z1) x2 <- rnorm(10,1,1) y2