Outlier Detection via Trimming of Mutual Reachability Minimum Spanning Trees

Implements an anomaly detection algorithm based on mutual reachability minimum spanning trees: 'deadwood' trims protruding tree segments and marks small debris as outliers; see Gagolewski (2026) < https://deadwood.gagolewski.com/>. More precisely, the use of a mutual reachability distance pulls peripheral points farther away from each other. Tree edges with weights beyond the detected elbow point are removed. All the resulting connected components whose sizes are smaller than a given threshold are deemed anomalous. The 'Python' version of 'deadwood' is available via 'PyPI'.

Joint Change Point Detection

Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null hypothesis of the absence of change points. In case of rejection performs an algorithm for change point detection. Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.

Dimension Reduction for Outlier Detection

A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. This is not an outlier detection method; rather it is a pre-processing method for outlier detection. It brings outliers to the fore-front using fewer basis vectors (Kandanaarachchi, Hyndman 2020) .

Bloom Detecting Algorithm

The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) .

Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R

A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R.

Optimizing Acoustic Signal Detection

Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) .

Hyperlink Automatic Detection

Automatic detection of hyperlinks for packages and calls in the text of 'rmarkdown' or 'quarto' documents.

PharmacoVigilance Signal Detection

A collection of several pharmacovigilance signal detection methods extended to the multiple comparison setting.

Backward Procedure for Change-Point Detection

Implements a backward procedure for single and multiple change point detection proposed by Shin et al. . The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.

Leave One Out Kernel Density Estimates for Outlier Detection

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.