Nov 8,2025The Ssarkartrim package provides a robust trimmed-k mean
estimator for numeric data. It is designed to reduce the influence of
outliers by removing the k smallest and k
largest values before computing the mean. This vignette walks through
installation, usage, and interpretation of the function.
To install the package from source:
```r
install.packages(“Ssarkartrim_1.0.0.tar.gz”, repos = NULL, type = “source”)
library(Ssarkartrim)
kTrimMean()kTrimMean(dat, k)
dat : A numeric vector.
k : Number of smallest and largest values to trim.
Returns the trimmed-k mean of the data. If 2k >= length(dat), the function returns NA with a warning
set.seed(5400)
dat <- rexp(20, rate = 0.5)
kTrimMean(dat, k = 2)
[1] 1.592987
This trims the 2 smallest and 2 largest values from dat
and computes the mean of the
remaining 16 values.
mean(dat)
median(dat)
kTrimMean(dat, k = 2)
This shows how kTrimMean() provides a middle ground:
mean() is sensitive to outliers.
median() is robust but may ignore distribution
shape.
kTrimMean() trims extremes while preserving central
tendency.
small_dat <- c(1, 2, 3, 100, 200)
kTrimMean(small_dat, k = 1)
This trims the the lowest and largest values
short_dat <- c(5, 10)
kTrimMean(short_dat, k = 1)
[1] NA
Warning message:
Not enough data to trim k smallest and largest values.
This shows the function’s built-in safeguard when trimming exceeds available data.
Robust estimation in small samples
Outlier-resistant summary statistics
Teaching robust statistics in coursework
Comparing estimators in simulation studies
This package was built using:
devtools::create()
Roxygen2 for documentation
document() to generate .Rd files
R CMD build and R CMD check –as-cran for validation
Rd2pdf to generate the manual
usethis::use_vignette() to create this vignette
The Ssarkartrim package offers a simple, reproducible, and pedagogically useful implementation of the trimmed-k mean.