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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.
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
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"
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
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, Sanderson, Hyndman 2024)
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.
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,
Spatially Explicit Capture-Recapture
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
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)