• you find a list of available reference tables by region and by item here.

• version 0.6.4 from cran via install.packages() or the respective menu

library(childsds)
## generate example data
x <- data.frame(height=c(50,100,60,54),
		  sex=c("m","f","f","m"),
		  age=c(0,2.9,0.6,0.2))
x$height.sds <- sds(value = x$height,
		      age = x$age,
		      sex = x$sex, male = "m", female = "f",
		      ref = who.ref, item = "height")
head(x)

#+RESULTS[46a6ba3828dbb6c977bc976a6280e0b191bc02ee]:

  height sex age  height.sds
1     50   m 0.0  0.06116878
2    100   f 2.9  1.54150151
3     60   f 0.6 -3.26293906
4     54   m 0.2 -2.82189275

library(childsds)
head(tab <- make_percentile_tab(ref = nl4.ref,
				  item = "heightM",
				  perc = c(5,50,95),
				  age = 1:3))

     sex age perc_05_0 perc_50_0 perc_95_0 nu       mu      sigma
1   male   1  72.82291  77.15261  81.48232  1 77.15261 0.03411775
2   male   2  82.10371  87.67000  93.23629  1 87.67000 0.03860000
3   male   3  89.97701  96.28000 102.58299  1 96.28000 0.03980000
4 female   1  70.58366  74.89305  79.20245  1 74.89305 0.03498225
5 female   2  82.06492  86.76000  91.45508  1 86.76000 0.03290000
6 female   3  89.41744  94.83000 100.24256  1 94.83000 0.03470000

library(childsds)
head(tab <- make_percentile_tab(ref = nl4.ref,
				  item = "heightM",
				  perc = c(5,50,95),
				  age = seq(0,20,by=0.1),
				  stack = T))

  age  sex  variable    value
1 0.0 male perc_05_0 47.82905
2 0.1 male perc_05_0 51.65139
3 0.2 male perc_05_0 55.37913
4 0.3 male perc_05_0 58.68443
5 0.4 male perc_05_0 61.60275
6 0.5 male perc_05_0 64.21947

library(ggplot2)
ggplot(tab, aes( x = age, y = value, group=paste(sex, variable))) +
    geom_line(aes(linetype = sex)) +
    theme_classic() +
    theme(legend.position = c(0.1,0.8))

#+RESULTS[9e68fab1cedee6b9007e7fe1696cac77f23d3ef4]: