Fast Principal Component Analysis for Outlier Detection

Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) and later revised in (Privé, 2020) .

Functions for the Detection of Spatial Clusters of Diseases

A set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics.

Nonparametric Multiple Change Point Detection Using WBS

Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" . This uses a version of Wild Binary Segmentation to detect multiple location-scale (i.e. mean and/or variance) change points in a sequence of univariate observations, with a strict control on the probability of incorrectly detecting a change point in a sequence which does not contain any.

Implementation of the Unsupervised Smooth Contour Line Detection for Images

An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). The algorithm is explained at .

Abrupt Change-Point or Aberration Detection in Point Series

Offers an interactive function for the detection of breakpoints in series.

Calculate 3D Contour Meshes Using the Marching Cubes Algorithm

A port of the C++ routine for applying the marching cubes algorithm written by Thomas Lewiner et al. (2012) into an R package. The package supplies the contour3d() function, which takes a 3-dimensional array of voxel data and calculates the vertices, vertex normals, and faces for a 3d mesh representing the contour(s) at a given level.

Detect Marine Heat Waves and Marine Cold Spells

Given a time series of daily temperatures, the package provides tools to detect extreme thermal events, including marine heat waves, and to calculate the exceedances above or below specified threshold values. It outputs the properties of all detected events and exceedances.

Time Series Outlier Detection

Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.

Multivariate Outlier Detection in Contingency Tables

Outlier detection in, possibly high-dimensional, categorical data following Mads Lindskou et al. (2019) .

Error Detection in Science

Test published summary statistics for consistency (Brown and Heathers, 2017, ; Allard, 2018, < https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>; Heathers and Brown, 2019, < https://osf.io/5vb3u/>). The package also provides infrastructure for implementing new error detection techniques.