Event Detection Framework

Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.

Outlier Detection via Trimming of Mutual Reachability Minimum Spanning Trees

Implements an anomaly detection algorithm based on mutual reachability minimum spanning trees: 'deadwood' trims protruding tree segments and marks small debris as outliers; see Gagolewski (2026) < https://deadwood.gagolewski.com/>. More precisely, the use of a mutual reachability distance pulls peripheral points farther away from each other. Tree edges with weights beyond the detected elbow point are removed. All the resulting connected components whose sizes are smaller than a given threshold are deemed anomalous. The 'Python' version of 'deadwood' is available via 'PyPI'.

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) .

Joint Change Point Detection

Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null hypothesis of the absence of change points. In case of rejection performs an algorithm for change point detection. Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.

Bloom Detecting Algorithm

The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) .

Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R

A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R.

Read and Write 'FreeSurfer' Neuroimaging File Formats

Provides functions to read and write neuroimaging data in various file formats, with a focus on 'FreeSurfer' < http://freesurfer.net/> formats. This includes, but is not limited to, the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.

Optimizing Acoustic Signal Detection

Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) .

Hyperlink Automatic Detection

Automatic detection of hyperlinks for packages and calls in the text of 'rmarkdown' or 'quarto' documents.

PharmacoVigilance Signal Detection

A collection of several pharmacovigilance signal detection methods extended to the multiple comparison setting.