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Base R provides weighted.mean() but nothing else.
wstats fills the gap with weighted versions of the
other common descriptive statistics.
# install.packages("devtools")
devtools::install_github("jgaeb/wstats")library(wstats)
x <- c(1, 2, 3, 4, 5)
w <- c(0.5, 1.0, 2.0, 1.0, 0.5) # unnormalised importance weights
weighted_var(x, w)
weighted_sd(x, w)
weighted_quantile(x, w, probs = c(0.25, 0.5, 0.75))
weighted_median(x, w)
weighted_mad(x, w)
weighted_skewness(x, w)
weighted_kurtosis(x, w)
y <- c(2, 3, 1, 5, 4)
weighted_cov(x, y, w)
weighted_cor(x, y, w)All functions currently use a population formula —
weights are treated as probability masses of a discrete distribution,
not survey sampling weights. Concretely, variance is
Σ(ŵᵢ (xᵢ − μ)²) where ŵᵢ = wᵢ / Σwⱼ, with no
Bessel correction. This is appropriate for importance sampling and the
Bayesian bootstrap; support for survey/frequency weights may be added in
a future release.
Computationally intensive routines are implemented in C++ via cpp11.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.