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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)
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.
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
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"
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,
Detect Heatwaves and Cold-Spells
The different methods for defining, detecting, and categorising the extreme events
known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016)
Same Species Sample Contamination Detection
Imports Variant Calling Format file into R. It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected.
Probability of Detection for Grab Sample Selection
Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988)
Probability of Detection for Qualitative PCR Methods
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' < http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015)
Sleep Cycle Detection
Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979)