Is there R method similar to ArcGIS StandardDistance_stats?

Question

I am finding some methods that can provide simple output.

I found a useful method StandardDistance_stats in ARCGIS, but the result is not what i am looking for, I only need the result of a lat/lng point and the size of the circle in number form, so I can plot it to my web map.

Now I am using geomean in geosphere package, it gives the mean of lat/lng points but still missing the size of the circle.

Edit: I found a solution for it, but I am stuck in import R script into Java

d <- read.csv(file="./points.csv", header=TRUE, sep=",")
x <- d$lng
y <- d$lat
w <- d$weight
df <- data.frame(x,y)
mean = geomean(df,w)
.sdd <- function(t,mean) {
    x <- t$lng
    y <- t$lat
    x <- x-mean[1]
    y <- y-mean[2]
    x <- x*x
    y <- y*y
    result <- (Reduce("+",x)/length(x))+ (Reduce("+",y)/length(y))
    result <- sqrt(result)
    return(result)
}
.sdd(d,mean)

Edit: I was trying to run R script file in java. i figured it out. It was because the permission problem, I was not allowed to access the root of C drive.

Base on this answer, I am not sure what should I modify in this line. I know that there are lots of Datums. if my list of lat/lng are mostly 22.0,144.0m is this line still suitable?

CRS.new <- CRS("+proj=somerc +lat_0=46.9524056 +lon_0=7.43958333 +ellps=bessel +x_0=2600000 +y_0=1200000 +towgs84=674.374,15.056,405.346 +units=m +k_0=1 +no_defs")

Answer 1

"Calculations based on either Euclidean or Manhattan distance require projected data to accurately measure distances." therefore it does not work with longitude latitude (or using a package like circular (Circular Statistics) for the mean and standard distance, and an appropriated distance )

With projected data (euclidean)

x = c(203595.92857397342, 203711.9297397244, 203992.57772137996, 203756.83341678928, 203610.89646632838, 203393.86202718143, 203524.83108528735, 203696.96184736944, 203363.9262424715, 203887.8024748952, 204018.77153300116, 204131.03072566338, 204426.6465996739)
y = c(89463.34428931243, 89343.60115047272, 89246.30985016546, 89152.76052294695, 89253.79379634294, 89343.60115047272, 89122.82473823702, 88991.85568013109, 89609.28123977332, 89687.86267463688, 89556.89361653096, 89901.15514069512, 89833.79962509777)
mean_centerX <- mean(x)
mean_centery <- mean(y)
standard_deviationX = sd(x)
standard_deviationY = sd(y)
standard_distance <- sqrt(sum(((x -mean_centerX)^2(y-mean_centerY)^2))/(length(x)))
library(plotrix)
plot(x,y)
points(mean_centerX,mean_centery,col="red",pch=16)
draw.circle(mean_centerX,mean_centery,radius=standard_distance,border="red",lwd=2)