Found 953 packages in 0.01 seconds
Tools for Cleaning Rectangular Data
A dependency-free collection of simple functions for cleaning rectangular data. This package allows to detect, count and replace values or discard rows/columns using a predicate function. In addition, it provides tools to check conditions and return informative error messages.
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.
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
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"
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)
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,
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.