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Title: Weighted Descriptive Statistics
Version: 0.1.1
Description: Weighted versions of common descriptive statistics (variance, standard deviation, covariance, correlation, quantiles).
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: glue, rlang
Suggests: pkgdown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
LinkingTo: cpp11
NeedsCompilation: yes
Packaged: 2026-03-07 21:51:04 UTC; jgaeb
Author: Johann D. Gaebler [aut, cre]
Maintainer: Johann D. Gaebler <me@jgaeb.com>
Repository: CRAN
Date/Publication: 2026-03-12 08:20:02 UTC

wstats: Weighted Descriptive Statistics

Description

Weighted versions of common descriptive statistics (variance, standard deviation, covariance, correlation, quantiles).

Author(s)

Maintainer: Johann D. Gaebler me@jgaeb.com

Weighted correlation

Description

Weighted correlation coefficient, computed as weighted_cov(x, y, w) / (weighted_sd(x, w) * weighted_sd(y, w)).

Usage

weighted_cor(x, y, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

y

A numeric vector of observations (same length as x).

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, observations with any NA in x, y, or w are removed before computation.

Value

A single numeric value in [-1, 1].

Weighted covariance

Description

Computes the population (importance-weight) weighted covariance sum(w_hat * (x - mu_x) * (y - mu_y)).

Usage

weighted_cov(x, y, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

y

A numeric vector of observations (same length as x).

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, observations with any NA in x, y, or w are removed before computation.

Value

A single numeric value.

Weighted excess kurtosis

Description

Computes the population weighted excess kurtosis: sum(w_hat * ((x - mu) / sigma)^4) - 3.

Usage

weighted_kurtosis(x, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, paired NAs in x and w are removed before computation. If FALSE (default) and any NA is present, NA is returned.

Value

A single numeric value.

Weighted median absolute deviation

Description

Computes the weighted MAD as the weighted median of ⁠|x - median(x, w)|⁠, scaled by constant (default 1.4826 for consistency with stats::mad() under normality).

Usage

weighted_mad(x, w, na.rm = FALSE, constant = 1.4826)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, paired NAs in x and w are removed before computation. If FALSE (default) and any NA is present, NA is returned.

constant

Scale factor (default 1.4826).

Value

A single numeric value.

Weighted median

Description

Convenience wrapper: weighted_quantile(x, w, 0.5, na.rm).

Usage

weighted_median(x, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, paired NAs in x and w are removed before computation. If FALSE (default) and any NA is present, NA is returned.

Value

A single numeric value.

Weighted quantiles

Description

Computes weighted quantiles using a type-7 analog: observations are placed at the midpoints of their weight intervals in the cumulative weight distribution, rescaled to [0, 1], and quantiles are obtained by linear interpolation. For equal weights this matches quantile(x, type = 7).

Usage

weighted_quantile(x, w, probs = seq(0, 1, 0.25), na.rm = FALSE)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

probs

A numeric vector of probabilities in [0, 1].

na.rm

Logical. If TRUE, paired NAs in x and w are removed.

Value

A numeric vector of the same length as probs.

Weighted standard deviation

Description

Square root of weighted_var().

Usage

weighted_sd(x, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, paired NAs in x and w are removed before computation. If FALSE (default) and any NA is present, NA is returned.

Value

A single numeric value.

Weighted skewness

Description

Computes the population weighted skewness (Fisher's g1): sum(w_hat * ((x - mu) / sigma)^3).

Usage

weighted_skewness(x, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, paired NAs in x and w are removed before computation. If FALSE (default) and any NA is present, NA is returned.

Value

A single numeric value.

Weighted variance

Description

Computes the population (importance-weight) weighted variance sum(w_hat * (x - mu)^2) where w_hat = w / sum(w) and mu = weighted.mean(x, w).

Usage

weighted_var(x, w, na.rm = FALSE)

Arguments

x

A numeric vector of observations.

w

A numeric vector of non-negative weights (need not sum to 1).

na.rm

Logical. If TRUE, paired NAs in x and w are removed before computation. If FALSE (default) and any NA is present, NA is returned.

Value

A single numeric value.

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.