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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.
Health stats visible at Monitor.