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, .

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.

Explainable Outlier Detection Through Decision Tree Conditioning

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) . Loosely based on the 'GritBot' < https://www.rulequest.com/gritbot-info.html> software.

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) .

Analyzing Data with Cellwise Outliers

Tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) (open access) Hubert et al. (2019) (open access), Raymaekers and Rousseeuw (2021) (open access), Raymaekers and Rousseeuw (2021) (open access), Raymaekers and Rousseeuw (2021) (open access), Raymaekers and Rousseeuw (2022) (open access) Rousseeuw (2022) (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples", "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples", and "cellwise_weights_examples".

Hyperlink Automatic Detection

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

Analysis of Spatial Stratified Heterogeneity

Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025).

PharmacoVigilance Signal Detection

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