Package {gmeans}

Title:	G-means Clustering
Version:	0.1.0
Description:	Gaussian-means (G-means) clustering is a clustering algorithm that extends the k-means algorithm by automatically determining the number of clusters.
License:	MIT + file LICENSE
URL:	https://m-muecke.github.io/gmeans/, https://github.com/m-muecke/gmeans
BugReports:	https://github.com/m-muecke/gmeans/issues
Depends:	R (≥ 4.1.0)
Imports:	stats
Suggests:	clue, data.table (≥ 1.15.0), GGally, ggfortify, ggplot2, knitr, mlr3cluster, mlr3misc, mlr3viz, paradox (≥ 0.6.0), R6, rmarkdown, testthat (≥ 3.3.0), withr (≥ 2.4.0)
VignetteBuilder:	knitr
Config/roxygen2/markdown:	TRUE
Config/roxygen2/version:	8.0.0
Config/testthat/edition:	3
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2026-07-25 17:58:44 UTC; mmuecke
Author:	Maximilian Mücke [aut, cre]
Maintainer:	Maximilian Mücke <muecke.maximilian@gmail.com>
Repository:	CRAN
Date/Publication:	2026-08-05 06:40:08 UTC

Anderson-Darling Normality Test

Description

Perform the Anderson-Darling normality test.

Usage

ad.test(x)

Arguments

Details

The Anderson-Darling test is an EDF omnibus test for the composite hypothesis of normality. The test statistic is

A^2 = -n -\frac{1}{n} \sum_{i=1}^{n} (2i - 1) [\ln(z_{i}) + \ln(1 - z_{n + 1 - i})]

where z_{i} = \Phi(\frac{x_{i} - \bar{x}}{s}). Here, \Phi is the cumulative distribution function of the standard normal distribution, and \bar{x} and s are mean and standard deviation of the data values. The p-value is computed from the modified statistic A^2_*=A^2 (1.0 + 0.75/n + 2.25/n^{2}) according to Table 4.9 in Stephens (1986).

Value

A list inheriting from classes "htest" containing the following components:

• statistic: the value of the statistic.

• p.value: the p-value of the test.

• method: the character string "Anderson-Darling normality test".

• data.name: a character string giving the name(s) of the data.

Source

Adapted from nortest::ad.test()

References

Stephens, A. M (1986). “Goodness-of-Fit-Techniques.” In D'Agostino, B. R (eds.), chapter Tests based on EDF statistics. CRC Press.

Thode, C. H (2002). Testing for normality, 1 edition. CRC Press. doi:10.1201/9780203910894.

See Also

stats::shapiro.test() for performing the Shapiro-Wilk test for normality. nortest::cvm.test(), nortest::lillie.test(), nortest::pearson.test(), nortest::sf.test() for performing further tests for normality. stats::qqnorm() for producing a normal quantile-quantile plot.

Examples

set.seed(123)
ad.test(rnorm(100, mean = 5, sd = 3))
ad.test(runif(100, min = 2, max = 4))

Compute Within-Cluster Sum of Squares

Description

Compute Within-Cluster Sum of Squares

Usage

compute_wss(object, newdata = NULL)

Arguments

Details

WSS is defined as

\sum_{i=1}^{n} \left\|x_{i} - \mu_{j(i)}\right\|^2,

where x_{i} is a data point and \mu_{j(i)} is the centroid of the cluster to which x_{i} is assigned. When new data is provided, the function predicts the nearest cluster for each new observation and computes the WSS for these points based on their predicted clusters.

Value

A numeric() vector with one within-cluster sum of squares per cluster, in the order of the rows of object$centers. Clusters with no assigned points contribute 0.

Examples

km <- kmeans(mtcars, 5)
compute_wss(km)
# or with new data
compute_wss(km, mtcars)

G-means Clustering

Description

Perform G-means clustering on a data matrix.

Usage

gmeans(x, k_init = 2L, k_max = 10L, level = 0.05, ...)

Arguments

Details

The G-means clustering algorithm is an extension of the traditional k-means algorithm that automatically determines the number of clusters by iteratively testing the Gaussianity of data within clusters. The process begins with a specified initial number of clusters (k_init) and iteratively increases the number of clusters until it reaches the specified maximum (k_max) or the data within clusters is determined to be Gaussian at the specified significance level (level).

The algorithm is outlined as follows:

1. Let C be the initial set of centers (usually C \leftarrow \{\bar{x}\}).

2. Perform k-means clustering on the dataset X using the current set of centers C, i.e., C \leftarrow \text{kmeans}(C, X).

3. For each center c_j, identify the set of data points \{x_i \mid \text{class}(x_i) = j\} that are assigned to c_j.

4. Use the Anderson-Darling test to check if the set of data points \{x_i \mid \text{class}(x_i) = j\} follows a Gaussian distribution at the confidence level \alpha.

5. If the data points appear Gaussian, keep c_j. Otherwise, replace c_j with two new centers.

6. Repeat from step 2 until no more centers are added.

Value

An object of class c("gmeans", "kmeans"). See stats::kmeans() for details.

References

Hamerly, Greg, Elkan, Charles (2003). “Learning the k in k-means.” In Thrun S, Saul L, Schölkopf B (eds.), Advances in Neural Information Processing Systems, volume 16. https://proceedings.neurips.cc/paper_files/paper/2003/file/234833147b97bb6aed53a8f4f1c7a7d8-Paper.pdf.

Examples

set.seed(123)
x <- rbind(
  matrix(rnorm(100, sd = 0.3), ncol = 2),
  matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)
)
colnames(x) <- c("x", "y")
cl <- gmeans(x)

Predict Method for G-means Clustering

Description

Predicted values based on the G-means clustering model.

Usage

## S3 method for class 'gmeans'
predict(
  object,
  newdata,
  method = c("euclidean", "manhattan", "minkowski"),
  p = 2,
  ...
)

Arguments

Details

The predict method for G-means clustering assigns new data points to the nearest cluster center identified by the G-means algorithm. The method uses the specified distance metric to calculate the distance between each new data point and all cluster centers, and then assigns each point to the cluster with the closest center.

The method argument specifies the distance metric to use. The following options:

• "euclidean": The Euclidean distance is the default metric used in the k-means and is defined as

  d(x, y) = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2}

• "manhattan": The Manhattan distance is defined as

  d(x, y) = \sum_{i=1}^{n} |x_i - y_i|

• "minkowski": The Minkowski distance is defined as

  d(x, y) = \left( \sum_{i=1}^{n} |x_i - y_i|^p \right)^{1/p},

  where p is a parameter that defines the distance type (e.g., p=2 for Euclidean, p=1 for Manhattan).

Value

An integer() vector with one cluster index per row of newdata.

Source

Adapted from clue

See Also

clue::cl_predict() to predict on a plain stats::kmeans() object.

Examples

set.seed(123)
x <- as.matrix(iris[, -5])
cl <- gmeans(x)

newdata <- x[1:10, ]
predict(cl, newdata)