| Title: | Multivariate Outlier Detection Methods |
| Version: | 0.1.2 |
| Description: | Provides methods for detecting multivariate outliers in numeric datasets. The package implements classical Mahalanobis distance, robust Minimum Covariance Determinant (MCD), and Principal Component Analysis (PCA)-based approaches for outlier detection. The methodology is informed by Aggarwal (2017) <doi:10.1007/978-3-319-47578-3> and Grentzelos, Caroni and Barranco-Chamorro (2020) <doi:10.1002/cmm4.1129>. Visualization functions are included to aid interpretation of detected outliers. Mahalanobis distance calculations are accelerated using 'C++' through 'Rcpp'. |
| URL: | https://github.com/SenuYasara/Multivariate_Outlier_Detection_R_Package |
| BugReports: | https://github.com/SenuYasara/Multivariate_Outlier_Detection_R_Package/issues |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| LinkingTo: | Rcpp |
| Imports: | Rcpp, stats, MASS, ggplot2, gridExtra, cowplot, rlang |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2026-06-15 23:10:57 UTC; pavan |
| Author: | Senuri Yasara [aut, cre], Pavanthi Sudasinghe [aut] |
| Maintainer: | Senuri Yasara <senuriyasara@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-06-16 06:50:43 UTC |
Detect Multivariate Outliers
Description
Detects multivariate outliers using Mahalanobis, Minimum Covariance Determinant (MCD), or PCA-based distances. Supports robust detection by computing distance scores for each observation and comparing them against a chi-squared cutoff at a specified significance level.
Usage
detect_multivariate_outliers(data, method = "mahalanobis", alpha = 0.975)
Arguments
data |
A numeric data frame or matrix. |
method |
Outlier detection method: "mahalanobis", "mcd", or "pca". |
alpha |
Significance level (default = 0.975). |
Value
A data frame combining the original input data with distances and outlier flags.
Examples
df_mtcars <- mtcars[, c("mpg", "hp", "wt" )]
head(df_mtcars)
## Mahalanobis Distance
result_mahal <- detect_multivariate_outliers(df_mtcars, method = "mahalanobis", alpha = 0.975)
## Minimum Covariance Determinant (MCD)
result_mcd <- detect_multivariate_outliers(df_mtcars, method = "mcd", alpha = 0.975)
## Principal Component Analysis (PCA)
result_pca <- detect_multivariate_outliers(df_mtcars, method = "pca", alpha = 0.975)
Plot Pairwise Outliers
Description
Generates 2D scatterplots for each pair of variables in the dataset, with outliers identified using Mahalanobis or MCD distances computed across all variables, without including each observation in its own distance calculation.
Usage
plot_outliers(data, method = c("mahalanobis", "mcd"), alpha = 0.975)
Arguments
data |
A numeric data frame or matrix. |
method |
Outlier detection method: "mahalanobis" or "mcd". |
alpha |
The quantile cutoff for identifying outliers (default 0.975). |
Value
A gtable object is returned invisibly. The function is primarily used for generating pairwise scatterplots with a shared legend as a side effect.
Examples
df_mtcars <- mtcars[, c("mpg", "hp", "wt" )]
head(df_mtcars)
## Pairwise Plots: Mahalanobis
plot_outliers(df_mtcars, method = "mahalanobis", alpha = 0.975)
## Pairwise Plots: MCD
plot_outliers(df_mtcars, method = "mcd", alpha = 0.975)