Detect Sensitive Points in the Tail

The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025).

Fast Covariance Estimation for Multivariate Sparse Functional Data

Multivariate functional principal component analysis via fast covariance estimation for multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) .

Robust Changepoint Detection for Functional and Multivariate Data

Detect and test for changes in covariance structures of functional data, as well as changepoint detection for multivariate data more generally. Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., & Chenouri, S. (2025) is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data.

R Interface to X-13-ARIMA-SEATS

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package.

Angle-Based Outlier Detection

Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.

Online Multivariate Changepoint Detection

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

Detecting Breakpoints and Estimating Segments in Trend

A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.

Detection of domains in HiC data

This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.

Detecting Correlated Genomic Regions

Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression.

Anomaly Detection in Temporal Networks

Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) .