Tools for Modeling Rate-Dependent Hysteretic Processes and Ellipses

Fit, summarize and plot sinusoidal hysteretic processes using: two-step simple harmonic least squares, ellipse-specific non-linear least squares, the direct method, geometric least squares or linear least squares. See Yang, F and A. Parkhurst, "Efficient Estimation of Elliptical Hysteresis with Application to the Characterization of Heat Stress" .

Bayesian Time-Stratified Population Analysis

Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) .

Wrangle, Analyze, and Visualize Animal Movement Data

Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli.

Estimation of Caribou Abundance Based on Radio Telemetry Data

Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990).

Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) .

Analysis of Chronological Patterns from Archaeological Count Data

A toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.

Create Sankey and Alluvial Diagrams Using 'ggplot2'

Sankey and alluvial diagrams visualise flows of quantities across stages in stacked bars. This package makes it easy to create such diagrams using 'ggplot2'.

ICS via a Shiny Application

Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) ) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2016) ) using a shiny app.

Two Event Mark-Recapture Experiment

Tools are provided for estimating, testing, and simulating abundance in a two-event (Petersen) mark-recapture experiment. Functions are given to calculate the Petersen, Chapman, and Bailey estimators and associated variances. However, the principal utility is a set of functions to simulate random draws from these estimators, and use these to conduct hypothesis tests and power calculations. Additionally, a set of functions are provided for generating confidence intervals via bootstrapping. Functions are also provided to test abundance estimator consistency under complete or partial stratification, and to calculate stratified or partially stratified estimators. Functions are also provided to calculate recommended sample sizes. Referenced methods can be found in Arnason et al. (1996) , Bailey (1951) , Bailey (1952) , Chapman (1951) NAID:20001644490, Cohen (1988) ISBN:0-12-179060-6, Darroch (1961) , and Robson and Regier (1964) .

Extremal Dependence Models

A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) , Marcon et al. (2016) , Marcon et al. (2017) , Marcon et al. (2017) and Beranger et al. (2021) . This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) .