R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(100,100,100,100,100,100,100,100,100,100,100,100,100.4,100.4,100.4,100.4,100.4,100.4,100.4,100.4,100.4,100.4,101.4,101.4,102,102,102.6,102.6,102.6,102.6,102.6,102.6,102.3,102.4,102.4,102.4,102.9,102.9,102.9,104.9,104.9,105.5,105.5,105.5,105.5,105.5,105.5,105.5,105.5,106.8,106.8,106.8,106.9,107.5,107.6,107.6,107.6,107.8,107.8,107.8)
> par1 = '12'
> par1 <- '12'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P. (2012), Standard Deviation-Mean Plot (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_smp.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 60
> (np <- floor(n / par1))
[1] 5
> arr <- array(NA,dim=c(par1,np))
> j <- 0
> k <- 1
> for (i in 1:(np*par1))
+ {
+ j = j + 1
+ arr[j,k] <- x[i]
+ if (j == par1) {
+ j = 0
+ k=k+1
+ }
+ }
> arr
      [,1]  [,2]  [,3]  [,4]  [,5]
 [1,]  100 100.4 102.0 102.9 105.5
 [2,]  100 100.4 102.0 102.9 106.8
 [3,]  100 100.4 102.6 102.9 106.8
 [4,]  100 100.4 102.6 104.9 106.8
 [5,]  100 100.4 102.6 104.9 106.9
 [6,]  100 100.4 102.6 105.5 107.5
 [7,]  100 100.4 102.6 105.5 107.6
 [8,]  100 100.4 102.6 105.5 107.6
 [9,]  100 100.4 102.3 105.5 107.6
[10,]  100 100.4 102.4 105.5 107.8
[11,]  100 101.4 102.4 105.5 107.8
[12,]  100 101.4 102.4 105.5 107.8
> arr.mean <- array(NA,dim=np)
> arr.sd <- array(NA,dim=np)
> arr.range <- array(NA,dim=np)
> for (j in 1:np)
+ {
+ arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
+ arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
+ arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
+ }
> arr.mean
[1] 100.0000 100.5667 102.4250 104.7500 107.2083
> arr.sd
[1] 0.0000000 0.3892495 0.2261335 1.1381804 0.6815201
> arr.range
[1] 0.0 1.0 0.6 2.6 2.3
> (lm1 <- lm(arr.sd~arr.mean))

Call:
lm(formula = arr.sd ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
   -10.3971       0.1057  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  NA/NaN/Inf in 'y'
Calls: lm -> lm.fit
Execution halted