Detecting Outliers in Network Meta-Analysis

A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis : - simple outlier and influential detection measures - outlier and influential detection measures by considering study deletion (shift the mean) - plots for outlier and influential detection measures - Q-Q plot for network meta-analysis - Forward Search algorithm in network meta-analysis. - forward plots to monitor statistics in each step of the forward search algorithm - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.

Isolate-Detect Methodology for Multiple Change-Point Detection

Provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the "deterministic + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) < https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Change Point Detection with Missing Values

A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) < https://drive.google.com/file/d/1a8sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>.

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).

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.

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.

Detecting Correlated Genomic Regions

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

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.