Spatiotemporal Boundary Detection Model for Areal Unit Data

Implements a spatiotemporal boundary detection model with a dissimilarity metric for areal data with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and spatial correlation is introduced at each time point through a conditional autoregressive (CAR) prior. Temporal correlation is introduced through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", by Berchuck et al (2018), . The paper is in press at the Journal of the American Statistical Association.

Agent-Based Bird Point Count Simulator

A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 ). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) , Solymos et al. (2013) , and Solymos et al. (2018) , and sound attenuation experiments by Yip et al. (2017) .

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.

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.

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) .

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) .

Univariate Outlier Detection

Detect outliers in one-dimensional data.

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.

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) .

Detection of Univariate Outliers

Well known outlier detection techniques in the univariate case. Methods to deal with skewed distribution are included too. The Hidiroglou-Berthelot (1986) method to search for outliers in ratios of historical data is implemented as well. When available, survey weights can be used in outliers detection.