Tools for Summarising and Analysing Soundscape Data

A variety of tools relevant to the analysis of marine soundscape data. There are tools for downloading AIS (automatic identification system) data from Marine Cadastre < https://hub.marinecadastre.gov>, connecting AIS data to GPS coordinates, plotting summaries of various soundscape measurements, and downloading relevant environmental variables (wind, swell height) from the National Center for Atmospheric Research data server < https://gdex.ucar.edu/datasets/d084001/>. Most tools were developed to work well with output from 'Triton' software, but can be adapted to work with any similar measurements.

Multivariate Extremes: Bayesian Estimation of the Spectral Measure

Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) and Sabourin, Naveau and Fougeres (2013) .

Visualize 'Confounder' Control in Meta-Analyses

Visualize 'confounder' control in meta-analysis. 'metaconfoundr' is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important 'confounders'. Then, they evaluate whether studies controlled for these variables well. 'metaconfoundr' is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more.

Efficient Voting Methods for Committee Selection

A fast 'Rcpp'-based implementation of polynomially-computable voting theory methods for committee ranking and scoring. The package includes methods such as Approval Voting (AV), Satisfaction Approval Voting (SAV), sequential Proportional Approval Voting (PAV), and sequential Phragmen's Rule. Weighted variants of these methods are also provided, allowing for differential voter influence.

The Gumbel-Hougaard Copula

Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).

Sourcing Archaeological Materials by Chemical Composition

Exploration and analysis of compositional data in the framework of Aitchison (1986, ISBN: 978-94-010-8324-9). This package provides tools for chemical fingerprinting and source tracking of ancient materials.

Validation Tools for Artificial Neural Networks

Methods and tools for analysing and validating the outputs and modelled functions of artificial neural networks (ANNs) in terms of predictive, replicative and structural validity. Also provides a method for fitting feed-forward ANNs with a single hidden layer.

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.

Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) and Komárek and Komárková (2014, J. of Stat. Soft.) . It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) , Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) , Jaspers, Komárek, Aerts (2018, Biom. J.) and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) .

Structural Bayesian Information Criterion for Graphical Models

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.