| Title: | Information-Dense Centroid Metrics |
| Version: | 0.1.0 |
| Description: | Traditional central tendency and dispersion measures such as the mean and standard deviation can be misleading for multimodal or outlier-contaminated data. This package implements information-theoretic alternatives that weight observations by their local probability density, focusing on the region containing the bulk of the data. The core functions include ic_weights() for kernel density or histogram based entropy weighting, ic_center() and ic_dispersion() for weighted location and spread, ic_combined() for a single information density score between zero and one, and ic_plot() for visualization. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | ggplot2, ggfx |
| URL: | https://github.com/ahmetkutuk1/infocentroid |
| BugReports: | https://github.com/ahmetkutuk1/infocentroid/issues |
| Config/roxygen2/version: | 8.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2026-10-25 18:09:55 UTC; TEKNO |
| Author: | Ahmet Kutuk [aut, cre] |
| Maintainer: | Ahmet Kutuk <ahmetkutuk@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-11-08 18:10:02 UTC |
Information-Dense Centroid
Description
Calculates the entropy-weighted center of a numeric vector.
Usage
ic_center(x, method = c("hist", "kde"), alpha = NULL, ...)
Arguments
x |
A numeric vector. |
method |
Density estimation method. |
alpha |
Power parameter for weights. |
... |
Additional arguments passed to density(). |
Value
A single numeric value.
Examples
x <- c(rnorm(90, 0, 0.5), rnorm(10, 10, 2))
ic_center(x, method = "kde")
Information Density Score
Description
Combines sharpness (inverse entropy) and signal-to-noise ratio (SNR) into a single score between 0 and 1.
Usage
ic_combined(x, method = c("hist", "kde"), alpha = NULL, ...)
Arguments
x |
A numeric vector. |
method |
Density estimation method. |
alpha |
Power parameter for weights. |
... |
Additional arguments passed to density(). |
Value
A list containing the combined score and sub-components.
Information-Dense Dispersion
Description
Calculates the dispersion based on the same entropy-weighted distribution used in ic_center.
Usage
ic_dispersion(x, method = c("hist", "kde"), alpha = NULL, ...)
Arguments
x |
A numeric vector. |
method |
Density estimation method. |
alpha |
Power parameter for weights. |
... |
Additional arguments passed to density(). |
Value
A numeric value representing dispersion.
Plot Information-Dense Centroid
Description
Creates a futuristic, dark-themed plot comparing the classical mean with the IDC.
Usage
ic_plot(x, method = c("hist", "kde"), alpha = NULL, ...)
Arguments
x |
A numeric vector. |
method |
Density estimation method. |
alpha |
Power parameter for weights. |
... |
Additional arguments passed to density(). |
Value
A ggplot object.
Summary of Information-Dense Centroid
Description
Prints a comprehensive summary of classical and IC metrics, and optionally generates the plot.
Usage
ic_summary(x, method = c("hist", "kde"), alpha = NULL, plot = TRUE, ...)
Arguments
x |
A numeric vector. |
method |
Density estimation method. |
alpha |
Power parameter for weights. |
plot |
Logical. Whether to display the plot. Default is TRUE. |
... |
Additional arguments passed to density(). |
Value
Prints summary to console and returns the plot invisibly.
Internal Weights Calculation
Description
Computes the entropy-weighted distribution used by the IC metrics.
Usage
ic_weights(x, method = c("hist", "kde"), alpha = NULL, ...)
Arguments
x |
A numeric vector. |
method |
Density estimation method. |
alpha |
Power parameter for weights. |
... |
Additional arguments passed to density(). |
Value
A list of values, weights, entropy, and alpha.