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) .

Adaptive Approaches for Signal Detection in Pharmacovigilance

A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al .

Collinearity Detection in a Multiple Linear Regression Model

The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.

Change Point Detection

Time series analysis of network connectivity. Detects and visualizes change points between networks. Methods included in the package are discussed in depth in Baek, C., Gates, K. M., Leinwand, B., Pipiras, V. (2021) "Two sample tests for high-dimensional auto-covariances" and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023) “Detecting functional connectivity changes in fMRI data” .

Implementation of the Harris Corner Detection for Images

An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at . The package allows to detect relevant points in images which are characteristic to the digital image.

Change-Points Detections for Changes in Variance

Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, .