lookout - Leave One Out Kernel Density Estimates for Outlier Detection
Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.
Last updated 7 months ago
5.26 score 30 stars 2 packages 9 scripts 283 downloads
dobin - Dimension Reduction for Outlier Detection
A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. This is not an outlier detection method; rather it is a pre-processing method for outlier detection. It brings outliers to the fore-front using fewer basis vectors (Kandanaarachchi, Hyndman 2020) <doi:10.1080/10618600.2020.1807353>.
Last updated 2 years ago
detectiondimensionoutlierreduction
4.81 score 13 stars 8 scripts 235 downloadsstxplore - Exploration of Spatio-Temporal Data
A set of statistical tools for spatio-temporal data exploration. Includes simple plotting functions, covariance calculations and computations similar to principal component analysis for spatio-temporal data. Can use both dataframes and stars objects for all plots and computations. For more details refer 'Spatio-Temporal Statistics with R' (Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).
Last updated 1 years ago
4.70 score 5 stars 7 scripts 155 downloadsoddnet - 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) <arXiv:2210.07407>.
Last updated 6 months ago
4.22 score 3 stars 11 scripts 135 downloads
airt - Evaluation of Algorithm Collections Using Item Response Theory
An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.
Last updated 8 months ago
4.18 score 1 packages 8 scripts 365 downloads
outlierensembles - A Collection of Outlier Ensemble Algorithms
Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549> are also included.
Last updated 3 years ago
4.18 score 3 stars 7 scripts 149 downloads
netseer - Graph Prediction from a Graph Time Series
Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2024) <doi:10.48550/arXiv.2401.04280>.
Last updated 11 days ago
3.54 score 2 scripts 145 downloadseventstream - Streaming Events and their Early Classification
Implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.
Last updated 3 years ago
3.18 score 3 stars 133 downloads